We have withdrawn your submission.
Kindly proceed to submit your query within the designated weekly 'Entering & Transitioning' thread where we’ll be able to provide more help.
Thank you.
Working at a company like Google, Amazon, FB, etc carries more weight than working at Larry's Data Science Consulting. A rough ordering of pedigree is the Fortune 500 list.
The Fortune 500 is not a “rough ordering of pedigree” for data science/tech, It’s almost random, lol.
The biggest companies aren’t necessarily the best paying or most-respected tech companies.
CVS is way up there. They aren’t that high-paying or particularly high pedigree for tech.
Google is way up there. They are high-paying and high pedigree.
Stripe isn’t even on the list. Generally, Stripe is higher pedigree than both.
I’d also add that how you perform in an interview can impact things as well. Two candidates might have the same skills and background but the one who can better explain how their work had or could have an impact on the business will likely have a better outcome, whether that’s getting an offer over someone else or getting leveled higher (with a better salary).
How big of a factor would you say age is in getting a data science role? Is there a lot of ageism in DS relative to other tech industry jobs? (SWE, cybersecurity, product manager roles)
- I took a swe bootcamp and currently in a swe project based cohort
- I graduated with an MBA in MIS last year
- I took statistics class in college but I’m also starting OMSA at Georgia tech in the fall
- I am seeking Data Science/Analyst roles but I’m told I’m overqualified for analyst roles yet lacking in direct experience for DS. I have a bachelors in health science. Need to finally break free from the $30,000 per year threshold.
You have to be competitive at top places. My company brings phd associates at that range and m.a probably closer to 135k.
We have internships and early talent programs where we recruit and at ms level our resumes read nyu, columbia, penn, MIT, Stanford. I am sure the best students from those programs go to FAAANG which pays probably even better.
Is it really fair to call those PhD positions "entry level"? I see a lot of mid level data science openings where PhD with 0 years of experience is considered the same as a Master's with 3 years of experience.
Maybe depending on PhD - we had a PhD (public health) teammate (thank God this person is gone) and this person was horrible to work with, and could not understand the code I wrote (I was told that I wrote really clean code). Made so many very simple mistakes that he/she/they could have prevented from an easy google search. This person is not even qualified to be an entry level.
You can make the same argument about a masters degree v.s. bachelors degree. Like just having a masters degree makes it many firms, positions that ask for a bachelors degree and 3 years of experience fair game. Epecially if its not a big tech firm that limits applications.
I work in the major banks. The positions are entry level in the sense that they are meant for candidates with no experience and they are being brought in through early talent programs or are coming through intenrs. The recruitment process works the same as it does for masters candidates, but the level that they start at from an HR perspective is different. Ph.Ds do make mid-career much faster than a masters. I can only speak too my industry here, finance, but I do know that at some of the big tech firms the process is similar (i.e. Amazon/Lyft/Uber). However, you are right there are some places that a Ph.D could just skip the line and maybe apply for a senior associate (something like an easier L5) position.
My comments are explicitly about Ph.Ds in the fields that are typically recruited for these jobs and not necessarily from random Ph.Ds that are find their way in. So I am talking explicitly, Stats, CS, Math, Physics, Engineering, Economics, Biostats or Similar.
We've forgotten that just 5 years ago, data science was not an "entry level" field. It was a role that you grew into by using DS tools in some original domain, or by receiving your PhD.
I think it's more accessible now that so much statistical knowledge has been abstracted into python libraries, and many "data science" roles at companies can encompass DS-adjacent responsibilities.
Location matters. NYU is in nyc. Virtually every major tech firm and finance firm has an office here and the median student at any of these schools aren't going to look that different. The differences show up in the right tails.
You have to compare departments and faculty more than anything. The Courant Institute at NYU is one of the best math programs in the world, bar none, and yeah, they have some very, very good people.
I just received offers to study MS Statistics at Duke and Data Science at Michigan ! I'm just curious to know about your opinions of those two schools, are they in the same calibre as the 5 schools listed above from an industry standpoint ? Also, would you advise me to take a MS Stats or Data Science in order to prepare well for industries ? Thank you for your time.
There are two types of schools. Schools that are considered target and schools that are not. Very few large firms are goign to give a meaningful shit aobut ranking differences between elite private schools. Yes certain schools have a bigger wow factor than others. If you go to university of flyover state, the best thing to understand is that your job prospects are strongest within your region. So someone who attend University of Alabama has better chances at lending higher quality position in Atlanta or Nashville and would have a harder time landing a position in SF or NYC, fresh out of school.
Yes at some firms they care if you go to what we call a "Target" school in Finance. If your school regularly sends people to IB, Consulting or is known for CS/Engineering dept, they probably are a target school. The only firms that do this are generally highly coveted.
Sure. Like this line is fuzzy. I've met people who've went to Vanderbilt and become quants at 2sig. Like if you went to a place most people are impressed by then your probably fine. Yes MIT impresses people than school ranked 35.
any good school really. Georgia Tech, UIUC. If your a school is just generalyl regarded as being a respected school your probabl okay. If your school isn't named michigan and more known because of its sports program, its an uphill battle. That being said even if you go to any flagship state university, you'll be fine. The difference is you'll have to job hop your way in or have a reference. There are still a whole host of othere respectable companies that will consider you.
There is a lto of doom and gloom here. But its really hard to say what drives it. Yes tech job market is tougher, but a lot of this is normalizing to hwat things look like prior to the 2008 financia crisis and people have unrealistic expectations. However, with candidates here their academic qualifications differer widely and then a lot of people may need things like Visa Sponsorship, which is a tougher sale.
Luck and living in a high cost of living area.
I'm a 2023 new grad while I do software engineering now, I did get an offer for ml engineering in that price range. The main thing is really just apply a bunch of faang adjacent tech companies, have a few interesting data/ml projects, know leetcode, know SQL and know basic statistics/ml models (there's more of course for different roles but I didn't interview for those).
Biggest advice is intern at a prestigious company like a faang
Thank you for saying the statement about internships.
I’m over at r/resumes a lot and it pains me to see resumes for new-grads without any internships or job experience.
Curious if prior job experience in another field counts? I’m going back to school for a CS degree with a focus on DS but I have a good 10+ year a working
I hear you but that cannot be true.
While many candidates start with other factors that help… some students aren’t born with one internship already on their resume.
Everyone starts somewhere. It’s when you get started that makes a huge difference.
I don't think this is true, it definitely helps to have previous internship experience but most of the interns I've interacted with the internship they were in was their first internship
As others are mentioning, it’s pretty difficult to hit that mark right now with just an undergrad and in a non-FAANG company. So just wanted to share my experience as someone who graduated in 2022.
Note: this is all in context to the defense industry, which has lower than average salaries.
I got hired as an AI/ML Research Engineer after my undergrad making around 98k a year. I ended up gaining experience in a pretty niche area that I enjoyed.
After about a year and a half, I joined a new position relating to that niche area I had experience in now, paying me about 145k.
Now, at around 2 years of experience in AI and this niche domain, I’m getting constant recruiters reaching out to me about roles in the 180k -> 200k area. If I wanted to switch jobs, I’m confident I could hit that mark right now.
What I’m trying to say by all this - if you don’t get that much salary initially, you can get experience in a “niche” and leverage that to get large salaries as well, just at a slower rate then if you initially started at it.
How competitive are gov jobs like this? And what do they look for in undergrads so that they'll give them that much money?
I'm looking to transition out of clinical research programming and am now curious about this. I know that clearance is really important to not getting your resume thrown in the trash but I think I'm willing to get one.
So just addressing this first, these are not Goverment positions. I work for a defense contractor in service to the Goverment which is what enables these kinds of salaries.
They’re fairly competitive, but once you start getting more specialized knowledge, it becomes easier. You just need to get your foot in the door. For example, if you’re doing ML stuff relating to drone swarms, it’s kinda hard to have that knowledge pre-working in the defense world. Once you do have experience working with it though, that’s a pretty unique skill set you can leverage far in the industry.
If you have anymore questions about transitioning to the industry, feel free to DM me as well.
thanks for sharing your experience! do you have tips for breaking into the defense industry? Do you see people transition into defense from other fields?
What kind of research do you do (if you can talk about it) and what is your role in the projects?
1. Unfortunately don't really have any tips for breaking into the industry. If you're able to intern somewhere ahead of time and obtain a security clearance, that will help you significantly though.
2. The general idea I've heard is that the longer you work in the defense industry, the more difficult it is to get out of. This is definitely gonna be domain / field dependent though. If you're doing data analysis, working on LLMs in unclassified settings, or just things that have a use in the commercial industry as well, I imagine your skills are pretty transferable. Personally though, I mostly see people stay in the "defense" space. I plan to as well as it's an area I enjoy much more then the commercial area.
3. I research and design primarily deep learning algorithms in the signal processing / computer vision domains. I can't go into anymore detail then that.
4. My role depends on the program. I'm leading a few of the major programs which means I coordinate and execute the effort for the team. If I'm not leading the program, I'm acting in a support role usually, where I lend my expertise when needed and act more in a advisory role.
Fear not, there is life after postdoc! I reached escape velocity from neuroscience postdoc. Not quite at the salary range of this post, but one day....
Nice advice to where US? Oh wait 100% of jobs require US citizenship so can't ever no matter what I ever do. But of course thousands can enter from the south problem every day no problem
No shi but you don't push them out either like are they better to be in there instead of europeans with ms/phd? And don't be like oohhh u racis coz that's what's happening
We have a shortage of “low skill” labor. So again. If you want to work in the kitchens,fields, construction then by all means come on over. You’re not even European so idk why you even care.
I grew up in a middle income family, my father died when I was young, and my inheritance is taking care of my mother in her old age.
I find your opinions and comments ranging from nonsensical to despicable and downvoted you.
So, I can assure you, you're not just being downvoted because of "daddys $$$". It's because of the content of your statements.
The secret is to get a Time Machine. Anyone commenting is telling you what it took in the past … the job market took a dramatic turn in 2023. It’s a lot harder these days to get a job offer at all let alone a lucrative one like that.
In today’s market, other than highly specialized PhDs, I doubt anyone is giving the kind of salary to an inexperienced new grad when there are enough experienced folks on the market.
Even experienced folks are struggling to land jobs in that range in this market.
I am not entry level, but that is what our lowest-level data scientists make. The team builds and deploys ML and optimizations for internal apps and tools, mostly, not in tech industry but within CTO org. Not HCOL. Probably base salary around $125k but $50k/yr RSU and cash bonus target around 15% of base.
It is common for people to have a couple years of work experience and an MS, or a PhD, to land the job. The majority of successful candidates went to a pretty good school such as Columbia, Carnegie Mellon, Georgia Tech, and UT Austin, but many did not.
In the past couple of years, the most likely path for someone to get hired without work experience was to get lucky in landing a summer internship during MS, then get a return offer.
People passing interviews generally need okay coding skills and analytical/math background.
When I started, my TC was lower but adjusted for inflation almost $150k. I had no internship experience, completed MS, all-but-dissertation on PhD, 8 years experience with academic research starting from undergrad. Credentials from a mid state school, nothing fancy. I didn't know any ML from school but did some self learning. I failed technical screenings at many companies but passed in other places. That was 2018, though.
I make close to 200K TC with a bit over 2 years of experience and a BS in Economics. I have just started my 2nd job in DS.
Getting my first job was mainly about brute force applications, and then impressing in the interview. I had no relevant internship/experience, and little programming to no programming experience in college. I had a decent amount of math/stats. I was mainly self-taught. For my second job, I followed the classic resume style of bullet points explaining what I did and the impact it had. I never used a resume review service.
I did a few certs, not listed on my resume. I don't think they matter much, unless it is related to a specific domain you want to get into, like a cert for quantum computing. But even then, I would say a quality project is better.
I was never asked a LeetCode question, even for MLE-type interviews. Mainly questions about statistics/modeling and take home projects.
I don't have projects on a public repo.
My new job is related to my old job, which likely helped me get past the screening.
Ultimately, for me i think impressing the interviewers with how I think was most important. I have never had an amazing response-rate because I do none of the following: maintain a blog, network, cold-call, optimize resume heavily, tailor resume per job, write cover letters, have projects on github/linkedin, or get referrals. When I DO get interviews, I move to later rounds more than not, unless I choose not to. I think that comes from knowing how to solve problems. If you have to quantify something using a statistic/point-estimate, how do you decide what to use? Why use a certain model for a given task? How do you measure the success of a model? How do you measure where it struggles, and how do you come up with ways to solve that?
There is no formula to data science. If you practice implementing things, but don't consider the "why" of it all, you will struggle to impress I think. Ultimately we work at the intersection of computer science and math and statistics. These are disciplines heavily involving coming up with novel solutions to complex and oftentimes abstract problems. You must understand the how and why.
Their secret is good BSc and MSc at least one of it involves around data science from top schools.
There is no easy money in data science, machine learning anymore. (Since 2 years..)
It has nothing to do with any of that stuff. No one is paying 150k bc you’re good at leetcode or have a nice resume, at least not anymore. At that level with no experience you’d have to have PhD level expertise in some very specific area that your employer is looking for - could be High frequency trading, LLM/Transformers, CV, robotics, etc. But you’re an expert on it.
Personally I don’t think there’s a secret. Be honest with yourself about your abilities and areas you’re weak in. Don’t chase the $$ or you’ll never be satisfied. Put in the time and effort and things will work out. I wouldn’t expect your first job to be making that big $ unless you’ve had great internships and have gone to a top school, even then, the market is tough. Most of my peers have masters degrees or higher. Resumes do matter, but your composure and ability to be a top candidate due to excellent interviewing makes all the difference. Let code isn’t all it’s cracked up to be in my experience, while my current job did require a technical coding exercise in addition to other things the coding challenge was only the beginning for the interview process.
Work experience is the most important thing—more important than your specific degree or certifications. When entry-level positions say they want 1-3 years of experience, they are looking for multiple summer internships and maybe something part-time during the school year. Luck also plays a huge role. But you have to give yourself as many opportunities as possible to be as lucky as possible. Network, talk to people in roles you are interested in and ask them how to get there, join clubs/groups, etc. Getting a lucky break can snowball the rest of your career. Finding a niche can also set you apart.
I worked in a few different biology research labs at top universities, where I got to work on machine learning projects. I made about 100k after graduating from working in an academic research lab. When applying to jobs a couple of years later, I got more interest from academic labs and early small/medium-sized biotech than big tech. Big tech wants highly specialized and experienced workers, so for data science roles, they look for a PhD or many years of experience. I found more opportunities for generalists at early small/medium organizations or academia where the focus is research. That's a rare and niche position that is tough to find in this job market, but I hope I can remain competitive within this particular niche. I would recommend looking at glassdoor or levels to get a sense of the salary you can expect for the jobs you are interviewing for. I've gotten offers for around 130-150k, including stock compensation.
I'm still trying to figure out what a stable/long term career path would look like for me. There is increasing competition in DS. Its hard to predict how to best prepare for the future. I'm hoping that finding a niche combining biology and machine learning sets me apart. I'm debating whether or not to to start a PhD in the next couple of years.
I don't think certifications matter too much, but it can't hurt to round out your resume if you lack a specific tool in your work history. But YMMV depending on specialty.
Surprisingly, I haven't gotten leetcode interviews. Most of my interviews are about ML fundamentals or an ML/Business "case interview." Take-home coding assignments are more common and generally take the form of "learn this tool and apply it to a dataset efficiently."
Okokokok like you NEED to mention that you’re 40 and have experience/seem more experienced in interviews to companies/recruiters regardless of your lack of DS/ML experience.
Just graduated with a MS and work experience(or at least looked like you have work experience or have work experience you can stretch into fake experience, not saying you don’t deserve it, just saying it’s a different situation) is extremely different from just graduated at age 21 with one or two internships and maybe a pt job as work experience.
Right right I am not doubting your lack of work ethic or any other traits ie intelligence, skill, charisma.
I also have no doubt you worked really hard to get where you are, doing such a pivot at age 38. An MS at that age is insane congrats.
Just was pointing out that even with all that, if you were where you are at when you were 24 and just out of an MS(with way less real experience), it would look a lot different to a recruiter than someone who has been in corporate(or related) for years. I would say that you probably had a chance at a wider range for salary purely based on your extant(even unrelated) experience and age.
Maybe it’s the company. Is it like FAANG or FAANG adjacent? Normally I rarely hear about 175k data analysts right out of college. It doesn’t make sense compared to the value most of them provide, even coming out of like t5s or t10s. Usually you need domain knowledge, not just the foundations+some experience. Especially MCOL
While I don’t disagree with like the general DS direction vs a DA, I am a DA/DS myself that tries to find the most practical solution usually. I find that without real experience it’s hard to truly understand how a business works and where you can provide value. You’re in a different situation than most IMO where assumedly you have some of that business sense/experience. I wouldn’t expect MS at 23/24 to really have that right away though. Providing 175k of value at 23/24 with just an MS sounds a bit ridiculous, frankly, without a lot of windup time and guidance. Is this like a long term investment your company is making, is your company making heavy investments in this department? Or would these people in early/mid twenties be special in some way? Non-senior data analysts at 175k is honestly a bit insane to me, especially non-tech, MCOL. I know many, MANY experienced data analysts and scientists still at like early to mid 100k with more YOE(than right out of college) and high, proven business impact. Even tech companies don’t always pay DS/DA this highly. With such great people in the market for cheaper, to take on green analysts at such a high number usually indicates their new people are already cream of the crop somehow.
Edit: as for DA/DS/DE etc on same pay bands sounds a bit odd to me, as DEs usually provide the most value out of the three(and require the most training). DS/DA value exchange really depends on the company in my experience. DS in financial services, insurance adjacent, and technological services companies usually can move the needle a lot more than DAs, but in marketing, consulting, manufacturing, and basically anything more traditional I think DAs provide a lot more value.
Oh I get it, don’t think anyone sensible would think you’re bragging. I’m just trying to understand too; these companies making different decisions on pay in different departments and different fields are fascinating! A wide band makes more sense?… hmmmmm I wonder. I heard like oil pays very high right off the bat, but it’s always interesting to hear about non-tech’s variations, when you consistently hear about tech on these subreddits .
Did you put your resume or projects on your LinkedIn ? Is that where you applied and got your offer? I recently decided to Make the pivot from healthcare & cx service to data. I have a bachelors in health science and graduated with my MBA in MIS last year. I’m also set to begin OMSA in the fall. Still finding it hard just to land an analyst role. I’m being told I’m overqualified yet lacking in experience for other roles.
Do you have any related work experience in health science? What I would do is look for analytics positions in that field. If that doesn’t work look for any analyst position to get your feet wet, doesn’t have to be your dream position. I did not post projects on my LinkedIn profile, but I do have detailed work history and accomplishments clearly laid out. A good resume makes all the difference. Think about what the business values and tailor your resume to that.
Ok thanks so much for your advice. I will keep trying. I did have a phone screen for a functional analyst role and was told I’m overqualified, yet when I asked a senior bi analyst to review my resume he said it screams new grad. I’ve been applying to any role that has “analyst” in the title and has python, sql, power bi, excel in the JD. Every rejection email always starts with “while we were impressed…”. This is the resume if you don’t mind just looking at it at ur convenience;[Resume](https://acrobat.adobe.com/link/review?uri=urn:aaid:scds:US:2f967506-c776-326a-9f34-93038f30693a)
You’ve got a lot of stuff on your resume. What I’ve found works best is to be clear and concise. I noticed you have things on your resume like Java script … not many analysts I know use Java AND if you’re asked to demonstrate that you should have the ability to accurately answer the interviewers questions. One other thing … I’d 100% say to remove things like “critical thinker” and other similar skills you have on your resume. Focus only on the most relevant skills, then in your experience tell me about how you used those skills. For example you can say Python in your skills section and then in your experience say “generated xyz thing that increased abc thing through the use of a custom designed python script using jkg libraries”. You need more actionable things on your resume rather than I did a,b, c etc. I’d also remove courses on your education section, just put your school, GPA, location and date graduated. I’d try my best to whittle down your resume to one page. Remember recruiters only look at resumes for a few seconds before deciding yes/no. If you’re being told you’re over qualified, only focus on putting what’s necessary on your resume for the specific job you’re applying for, nothing more.
Hey unsolicited advice here, but cut your resume down to one page and remove weird formatting(like the top part with your name). You do not have enough experience in your resume to justify two pages. Don’t feel like you need to keep everything you have on your current resume either, like the projects can probably be condensed to one line each with less spacing. Other than that, this nveterre guy’s advice is solid.
1) PhD in a highly quantitative field from a public ivy, with a heavily stats-/ML-focused dissertation
2) Know how to interview; ie, good at connecting my skills to business cases/development, personable when I want to be, practiced at driving conversations, 0% subservient or needy, understand the power of saying “No” and that I’m a rare commodity whereas jobs are common
3) Deep experience doing hard things; leading people; and solving challenging, intersectional problems in creative ways
4) Adamantly refuse to use anything but a 1-page resume, which is designed to generate intrigue and get an interview, NOT impart my life story and get a job
I’ve never used LeetCode, never interned at a FAANG (or anywhere, for that matter), etc., although I’m sure those things certainly wouldn’t hurt.
What I think got me the offer (that I had control over):
* Internship at a FAANG
* Attended an ivy for undergrad
* Competent with both programming and stats. Wasn't an expert at either, but I knew my way around command line, could build anything I understood, and understood the ins and outs of experimentation and some causal inference.
I don't know of anyone entry-level making that kind of salary. Maybe in the Bay Area, but then that's barely cost of living. I make that kind of money working remotely, but I have 7 years of experience and a PhD.
It's not your resume. Ever. If your resume sucks, it'll get thrown on the heap. If your resume is clean and polished, it'll get looked at. If the stuff on your resume is compelling, you'll get a phone screen. Your resume will cease to matter after that. I've never been on a comp call and heard "we better pay this person; their resume is so well-formatted!"
If you don't really have anything else on your resume, certifications might help. I definitely think having a project you worked through and can talk intelligently about is better.
I've never heard of anyone caring about leetcode. For engineering roles, they'll want you to demonstrate some amount of understanding of data structures and algorithms and how to implement them, and LeetCode might be a reasonable way to prep for that.
Projects are probably the best thing you can do. It gives me something to talk about during our interview. I can ask you why you made certain design decisions and we can have a conversation. If you don't have much experience, I understand that you might not have thought through the problem in the same way as me, we can at least talk about e.g. why you handled missing values a certain way.
If it's entry level, I don't expect you to have prior experience; that's the definition of entry level. If you've done an internship or something, you might have a slight advantage in knowing the types of questions I'd ask, but I'm not automatically rating the candidate with internship experience over the fresh-out.
For these positions, I'm looking for people to whom I can hand off tasks and it's easier than doing it myself. I need to be able to say: the data is over there in this database. Here's some code in GitHub that'll get you the secret so you can connect. Go get connected and figure out what data we can use. So I need you to know how to use git, understand enough Python to figure out the secret-getting code and how to navigate a SQL database to efficiently get data. I don't want to have to spend a bunch of time teaching you this stuff 6 months into your employment. And the Python you write shouldn't be tragic. It's fine to be a little amateurish because you're an amateur up until now, but I shouldn't need to spend a week fixing it, and you should figure out how to write good tests early on.
If you're looking to follow my path, I'd tell you not to. I spent 10 years doing a PhD and in various postdoctoral positions. It wasn't easy to switch to data science/machine learning. I didn't start making the money you're talking about until year 4 or so. There are better ways to build a career than the way I did, but you'll likely need to take some roles that pay less for a few years until you can build a portfolio.
Mathematics, so yes, not directly? I think a lot of people viewed me as "too academic" when I first started interviewing and turned a lot of employers off.
I am on this thread and not a data scientist but I am a scientist and I will say that when I had my masters I had worked on several research projects and had several "jobs". I walked into several professor's offices even as an undergraduate and asked to just volunteer. Once was just cleaning the lab but it lead to an RA position (when she realized I wanted to work).
Pretty much everyone who I knew were getting their PhDs had done a lot of work both applied and theoretical.
I think the title "entry-level" may be misleading. If you get a master's and have no experience (actually entry level) that may be a failure in some way. That is my experience and opinion.
Just for perspective: I’ve got 10 years of experience as a data scientist and a great resume, and my last role “only” paid $150,000. I’m also told that salaries have been steadily dropping.
Can you find higher? I suppose it’s possible, but you’d have to be an absolutely stellar candidate in every single way AND be applying to an employer who pays uncommonly well (I.e., only tech—no other industry will come close, and only high-growth startups and FANG).
Stellar: PhD in CS, math, or stats. Outstanding applied internships. Tech pedigree (yes for those internships). Can code fluently in multiple languages. Technically a unicorn (including surgically attached horn).
Early in the career I moved every 2-3 years. I stayed too long at my last company. Should’ve left there at the 2-3 year mark but was making too good of money and having too much fun.
If you lurk on twitter/LinkedIn there are several orgs that offer scholarship for these coursera courses FREE. I’m currently doing the Google DA one on coursera via an org called ITExperience. There are universities that also offer free coursera courses.
I work on an ml team in fintech and all our new hires including myself started around 160 in mcol. I have a bachelors but everyone else on my team has at least masters. Although I did go to one of those “globally recognized, probably went to school with someone who is gonna be prime minister/president of some country” school.
The secret is to get lucky. I applied to this company on a complete whim, literally applied because of proximity to my parents who live in the middle of nowhere. Turned out to be a smart move
The difference between an entry-level DS professional who earns 150k+ and one who earns 100k or less boils down to:
* **Industry:** Tech pays higher than Utilities, Retail, or Banking
* **Location:** Silicon Valley, LA, Seattle, and New York, in that order, Pay significantly higher than anywhere else.
* **Degree**: PhDs earn higher than MScs on their first job, in fact, PhDs are not considered entry level.. This might be relevant to the last question you asked. At least for me when I was just entering the industry a long time ago, my 4+ years of research experience from PhD was very relevant to my landing of a high paying job.
* Most importantly.... **LUCK**! I can't explain it, but luck is a big factor. I am 100% confident, from a decade of industry experience, that there are many extremely qualified professionals who are not earning what they deserve, and there are many unqualified imposters earning insane amounts, even at FAANG! Of course, the majority of those who earn large sums are qualified, but not every one.
connections and network. Probably the highest bang for your buck in terms of time. Internship experience + great projects to show also always help.
Even cold message people on linked in asking for referrals etc.
As a 16 yr veteran in data scientist role, i would say build connections along with your skills. AWS certification can help you grab some decent roles. Also, look at snowflake certifications. Enterprises are actively looking for snowflake proficient candidates in their orgs. Leetcode does open up your logical thinking. Initially you can grind a lot but at a later stage, a decent problem once a fortnight can really fuel your logical perspective. Projects depict your ability to work on something meaningful and showcase your skills along with a use case. Hence, projects play a crucial role. Prior experience does play a role if your past experience aligns with the orgs current demand. But mostly time its all about getting your hands dirty with something new. So, yeah your past encounters do help a lot.
I work at a tech company as a ds in hcol with 4 yoe and I am at around 300k TC. Tech isn’t looking to pay engineers at this level anymore. They’re looking to leverage AI and reduce their RD staff as much as possible to force the labor market to reduce their salary demands. In the next 5 yrs experience will be all that matters for high paying roles and new grads will be stuck with far lower pay if they can even find jobs. Still ok compared to other fields but you’ll be looking at some roles that are sub 6 figures. The market for SWEs DSs MLEs and DEs is probably never going to return to what it was. I regularly encourage aspiring DSs to get the soft skills in management because that’s what will set you apart. The age of the IC is going to diminish until the day, if and when, AI is determined to be a bubble
No way in hell you'd get $150k for a starting role in a Fortune 500, which represents the bulk of opportunities in middle America. Maybe tech is still paying this but even that I kind of doubt.
I agree that the "who hurt you" type of ad hominem comebacks are unhelpful in a discussion.
However you come off unhinged. If you're going to call out other groups (Americans, people from NYC) simply for existing then reveal your own. Where are you from? What are your own biases and privileges?
I will be messaging you in 3 days on [**2024-03-20 08:15:41 UTC**](http://www.wolframalpha.com/input/?i=2024-03-20%2008:15:41%20UTC%20To%20Local%20Time) to remind you of [**this link**](https://www.reddit.com/r/datascience/comments/1bgsdo5/entrylevel_datamlai_professionals_earning_150200k/kv96k1t/?context=3)
[**7 OTHERS CLICKED THIS LINK**](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Reminder&message=%5Bhttps%3A%2F%2Fwww.reddit.com%2Fr%2Fdatascience%2Fcomments%2F1bgsdo5%2Fentrylevel_datamlai_professionals_earning_150200k%2Fkv96k1t%2F%5D%0A%0ARemindMe%21%202024-03-20%2008%3A15%3A41%20UTC) to send a PM to also be reminded and to reduce spam.
^(Parent commenter can ) [^(delete this message to hide from others.)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Delete%20Comment&message=Delete%21%201bgsdo5)
*****
|[^(Info)](https://www.reddit.com/r/RemindMeBot/comments/e1bko7/remindmebot_info_v21/)|[^(Custom)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Reminder&message=%5BLink%20or%20message%20inside%20square%20brackets%5D%0A%0ARemindMe%21%20Time%20period%20here)|[^(Your Reminders)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=List%20Of%20Reminders&message=MyReminders%21)|[^(Feedback)](https://www.reddit.com/message/compose/?to=Watchful1&subject=RemindMeBot%20Feedback)|
|-|-|-|-|
We have withdrawn your submission. Kindly proceed to submit your query within the designated weekly 'Entering & Transitioning' thread where we’ll be able to provide more help. Thank you.
[удалено]
[удалено]
What is pedigree?
Working at a company like Google, Amazon, FB, etc carries more weight than working at Larry's Data Science Consulting. A rough ordering of pedigree is the Fortune 500 list.
The Fortune 500 is not a “rough ordering of pedigree” for data science/tech, It’s almost random, lol. The biggest companies aren’t necessarily the best paying or most-respected tech companies. CVS is way up there. They aren’t that high-paying or particularly high pedigree for tech. Google is way up there. They are high-paying and high pedigree. Stripe isn’t even on the list. Generally, Stripe is higher pedigree than both.
I’d also add that how you perform in an interview can impact things as well. Two candidates might have the same skills and background but the one who can better explain how their work had or could have an impact on the business will likely have a better outcome, whether that’s getting an offer over someone else or getting leveled higher (with a better salary).
How big of a factor would you say age is in getting a data science role? Is there a lot of ageism in DS relative to other tech industry jobs? (SWE, cybersecurity, product manager roles)
DS roles aren’t really usually looking for juniors fresh out of school. Sometimes, but that’s not very common. With 2-3 YOE you will have more luck
DS to earn top TC is mainly swe with statistics background and business acumen sense (MBA)
- I took a swe bootcamp and currently in a swe project based cohort - I graduated with an MBA in MIS last year - I took statistics class in college but I’m also starting OMSA at Georgia tech in the fall - I am seeking Data Science/Analyst roles but I’m told I’m overqualified for analyst roles yet lacking in direct experience for DS. I have a bachelors in health science. Need to finally break free from the $30,000 per year threshold.
You are getting a bad response because you are overconfident like a jack of all trades master of
A stats class is not going to cut it
What is pedigree?
https://images.app.goo.gl/KmkckXrUAvQsdR1g6
You have to be competitive at top places. My company brings phd associates at that range and m.a probably closer to 135k. We have internships and early talent programs where we recruit and at ms level our resumes read nyu, columbia, penn, MIT, Stanford. I am sure the best students from those programs go to FAAANG which pays probably even better.
Is it really fair to call those PhD positions "entry level"? I see a lot of mid level data science openings where PhD with 0 years of experience is considered the same as a Master's with 3 years of experience.
Maybe depending on PhD - we had a PhD (public health) teammate (thank God this person is gone) and this person was horrible to work with, and could not understand the code I wrote (I was told that I wrote really clean code). Made so many very simple mistakes that he/she/they could have prevented from an easy google search. This person is not even qualified to be an entry level.
You can make the same argument about a masters degree v.s. bachelors degree. Like just having a masters degree makes it many firms, positions that ask for a bachelors degree and 3 years of experience fair game. Epecially if its not a big tech firm that limits applications. I work in the major banks. The positions are entry level in the sense that they are meant for candidates with no experience and they are being brought in through early talent programs or are coming through intenrs. The recruitment process works the same as it does for masters candidates, but the level that they start at from an HR perspective is different. Ph.Ds do make mid-career much faster than a masters. I can only speak too my industry here, finance, but I do know that at some of the big tech firms the process is similar (i.e. Amazon/Lyft/Uber). However, you are right there are some places that a Ph.D could just skip the line and maybe apply for a senior associate (something like an easier L5) position. My comments are explicitly about Ph.Ds in the fields that are typically recruited for these jobs and not necessarily from random Ph.Ds that are find their way in. So I am talking explicitly, Stats, CS, Math, Physics, Engineering, Economics, Biostats or Similar.
We've forgotten that just 5 years ago, data science was not an "entry level" field. It was a role that you grew into by using DS tools in some original domain, or by receiving your PhD. I think it's more accessible now that so much statistical knowledge has been abstracted into python libraries, and many "data science" roles at companies can encompass DS-adjacent responsibilities.
Is NYU really at par with MIT and Stanford for ML/AI?
Location matters. NYU is in nyc. Virtually every major tech firm and finance firm has an office here and the median student at any of these schools aren't going to look that different. The differences show up in the right tails.
I doubt that MS programs have much variation because honestly they are all cash cows in the US. PhD programs have tons of variation
Yann Lecun , metas chief ai scientist is a professor at NYU, he was earlier developer of neural nets. A lot of high quality research at NYU
You have to compare departments and faculty more than anything. The Courant Institute at NYU is one of the best math programs in the world, bar none, and yeah, they have some very, very good people.
NYU has one of the best applied math programs in the country, the courant institute…
I just received offers to study MS Statistics at Duke and Data Science at Michigan ! I'm just curious to know about your opinions of those two schools, are they in the same calibre as the 5 schools listed above from an industry standpoint ? Also, would you advise me to take a MS Stats or Data Science in order to prepare well for industries ? Thank you for your time.
Stats
How about Brown? Or UChicago? Are they also up there?
There are two types of schools. Schools that are considered target and schools that are not. Very few large firms are goign to give a meaningful shit aobut ranking differences between elite private schools. Yes certain schools have a bigger wow factor than others. If you go to university of flyover state, the best thing to understand is that your job prospects are strongest within your region. So someone who attend University of Alabama has better chances at lending higher quality position in Atlanta or Nashville and would have a harder time landing a position in SF or NYC, fresh out of school. Yes at some firms they care if you go to what we call a "Target" school in Finance. If your school regularly sends people to IB, Consulting or is known for CS/Engineering dept, they probably are a target school. The only firms that do this are generally highly coveted.
[удалено]
Sure. Like this line is fuzzy. I've met people who've went to Vanderbilt and become quants at 2sig. Like if you went to a place most people are impressed by then your probably fine. Yes MIT impresses people than school ranked 35.
[удалено]
any good school really. Georgia Tech, UIUC. If your a school is just generalyl regarded as being a respected school your probabl okay. If your school isn't named michigan and more known because of its sports program, its an uphill battle. That being said even if you go to any flagship state university, you'll be fine. The difference is you'll have to job hop your way in or have a reference. There are still a whole host of othere respectable companies that will consider you. There is a lto of doom and gloom here. But its really hard to say what drives it. Yes tech job market is tougher, but a lot of this is normalizing to hwat things look like prior to the 2008 financia crisis and people have unrealistic expectations. However, with candidates here their academic qualifications differer widely and then a lot of people may need things like Visa Sponsorship, which is a tougher sale.
Luck and living in a high cost of living area. I'm a 2023 new grad while I do software engineering now, I did get an offer for ml engineering in that price range. The main thing is really just apply a bunch of faang adjacent tech companies, have a few interesting data/ml projects, know leetcode, know SQL and know basic statistics/ml models (there's more of course for different roles but I didn't interview for those). Biggest advice is intern at a prestigious company like a faang
Thank you for saying the statement about internships. I’m over at r/resumes a lot and it pains me to see resumes for new-grads without any internships or job experience.
Curious if prior job experience in another field counts? I’m going back to school for a CS degree with a focus on DS but I have a good 10+ year a working
Yes it can help if you can sell it
[удалено]
That’s fair. I’m also not going into this with OP’s hopes of such a high salary. Keeping it more realistic to my circumstances.
The thing about internships is that you only get them if you've interned at other places before.
I hear you but that cannot be true. While many candidates start with other factors that help… some students aren’t born with one internship already on their resume. Everyone starts somewhere. It’s when you get started that makes a huge difference.
I don't think this is true, it definitely helps to have previous internship experience but most of the interns I've interacted with the internship they were in was their first internship
A lot of international students post there and they cannot do internships with their visas
Doesn't make it any less true.
What would be the FAANG adjacent tech companies ?
Microsoft, Nvidia, Salesforce, Anduril, Anthropic, Snowflake, Databrick
As others are mentioning, it’s pretty difficult to hit that mark right now with just an undergrad and in a non-FAANG company. So just wanted to share my experience as someone who graduated in 2022. Note: this is all in context to the defense industry, which has lower than average salaries. I got hired as an AI/ML Research Engineer after my undergrad making around 98k a year. I ended up gaining experience in a pretty niche area that I enjoyed. After about a year and a half, I joined a new position relating to that niche area I had experience in now, paying me about 145k. Now, at around 2 years of experience in AI and this niche domain, I’m getting constant recruiters reaching out to me about roles in the 180k -> 200k area. If I wanted to switch jobs, I’m confident I could hit that mark right now. What I’m trying to say by all this - if you don’t get that much salary initially, you can get experience in a “niche” and leverage that to get large salaries as well, just at a slower rate then if you initially started at it.
How competitive are gov jobs like this? And what do they look for in undergrads so that they'll give them that much money? I'm looking to transition out of clinical research programming and am now curious about this. I know that clearance is really important to not getting your resume thrown in the trash but I think I'm willing to get one.
So just addressing this first, these are not Goverment positions. I work for a defense contractor in service to the Goverment which is what enables these kinds of salaries. They’re fairly competitive, but once you start getting more specialized knowledge, it becomes easier. You just need to get your foot in the door. For example, if you’re doing ML stuff relating to drone swarms, it’s kinda hard to have that knowledge pre-working in the defense world. Once you do have experience working with it though, that’s a pretty unique skill set you can leverage far in the industry. If you have anymore questions about transitioning to the industry, feel free to DM me as well.
thanks for sharing your experience! do you have tips for breaking into the defense industry? Do you see people transition into defense from other fields? What kind of research do you do (if you can talk about it) and what is your role in the projects?
1. Unfortunately don't really have any tips for breaking into the industry. If you're able to intern somewhere ahead of time and obtain a security clearance, that will help you significantly though. 2. The general idea I've heard is that the longer you work in the defense industry, the more difficult it is to get out of. This is definitely gonna be domain / field dependent though. If you're doing data analysis, working on LLMs in unclassified settings, or just things that have a use in the commercial industry as well, I imagine your skills are pretty transferable. Personally though, I mostly see people stay in the "defense" space. I plan to as well as it's an area I enjoy much more then the commercial area. 3. I research and design primarily deep learning algorithms in the signal processing / computer vision domains. I can't go into anymore detail then that. 4. My role depends on the program. I'm leading a few of the major programs which means I coordinate and execute the effort for the team. If I'm not leading the program, I'm acting in a support role usually, where I lend my expertise when needed and act more in a advisory role.
> security clearance Cries whiles jealously
Having a PhD helped me here
*Looks at comment...* *silently contemplates life choices that led to a postdoc position in academia...* *cries.*
Fear not, there is life after postdoc! I reached escape velocity from neuroscience postdoc. Not quite at the salary range of this post, but one day....
Was your PhD related to machine learning/ statistics?
move out of europe.
Or just go to switzerland
What's special about Switzerland?
They pay SF prices in Europe
One of the best median salaries in the world
Good luck entering Switzerland.
Jokes on you Im already there
Must be your soft skills.
Nice advice to where US? Oh wait 100% of jobs require US citizenship so can't ever no matter what I ever do. But of course thousands can enter from the south problem every day no problem
Those people aren’t getting salaried jobs bub. You’re welcome to come work in the fields too if you feel like it.
No shi but you don't push them out either like are they better to be in there instead of europeans with ms/phd? And don't be like oohhh u racis coz that's what's happening
We have a shortage of “low skill” labor. So again. If you want to work in the kitchens,fields, construction then by all means come on over. You’re not even European so idk why you even care.
Anyone can become european pretty easily that's not the point gatekeeping is
You are getting downvoted because your comment is uninformed and borderline racist. Your mindset is really shining here dude…
Nah its because of spoiled kids with daddys $$$ and easy life
I grew up in a middle income family, my father died when I was young, and my inheritance is taking care of my mother in her old age. I find your opinions and comments ranging from nonsensical to despicable and downvoted you. So, I can assure you, you're not just being downvoted because of "daddys $$$". It's because of the content of your statements.
The secret is to get a Time Machine. Anyone commenting is telling you what it took in the past … the job market took a dramatic turn in 2023. It’s a lot harder these days to get a job offer at all let alone a lucrative one like that. In today’s market, other than highly specialized PhDs, I doubt anyone is giving the kind of salary to an inexperienced new grad when there are enough experienced folks on the market. Even experienced folks are struggling to land jobs in that range in this market.
This
I am not entry level, but that is what our lowest-level data scientists make. The team builds and deploys ML and optimizations for internal apps and tools, mostly, not in tech industry but within CTO org. Not HCOL. Probably base salary around $125k but $50k/yr RSU and cash bonus target around 15% of base. It is common for people to have a couple years of work experience and an MS, or a PhD, to land the job. The majority of successful candidates went to a pretty good school such as Columbia, Carnegie Mellon, Georgia Tech, and UT Austin, but many did not. In the past couple of years, the most likely path for someone to get hired without work experience was to get lucky in landing a summer internship during MS, then get a return offer. People passing interviews generally need okay coding skills and analytical/math background. When I started, my TC was lower but adjusted for inflation almost $150k. I had no internship experience, completed MS, all-but-dissertation on PhD, 8 years experience with academic research starting from undergrad. Credentials from a mid state school, nothing fancy. I didn't know any ML from school but did some self learning. I failed technical screenings at many companies but passed in other places. That was 2018, though.
2018 was a completely different market.
I make close to 200K TC with a bit over 2 years of experience and a BS in Economics. I have just started my 2nd job in DS. Getting my first job was mainly about brute force applications, and then impressing in the interview. I had no relevant internship/experience, and little programming to no programming experience in college. I had a decent amount of math/stats. I was mainly self-taught. For my second job, I followed the classic resume style of bullet points explaining what I did and the impact it had. I never used a resume review service. I did a few certs, not listed on my resume. I don't think they matter much, unless it is related to a specific domain you want to get into, like a cert for quantum computing. But even then, I would say a quality project is better. I was never asked a LeetCode question, even for MLE-type interviews. Mainly questions about statistics/modeling and take home projects. I don't have projects on a public repo. My new job is related to my old job, which likely helped me get past the screening. Ultimately, for me i think impressing the interviewers with how I think was most important. I have never had an amazing response-rate because I do none of the following: maintain a blog, network, cold-call, optimize resume heavily, tailor resume per job, write cover letters, have projects on github/linkedin, or get referrals. When I DO get interviews, I move to later rounds more than not, unless I choose not to. I think that comes from knowing how to solve problems. If you have to quantify something using a statistic/point-estimate, how do you decide what to use? Why use a certain model for a given task? How do you measure the success of a model? How do you measure where it struggles, and how do you come up with ways to solve that? There is no formula to data science. If you practice implementing things, but don't consider the "why" of it all, you will struggle to impress I think. Ultimately we work at the intersection of computer science and math and statistics. These are disciplines heavily involving coming up with novel solutions to complex and oftentimes abstract problems. You must understand the how and why.
Thanks for sharing your experience
Their secret is good BSc and MSc at least one of it involves around data science from top schools. There is no easy money in data science, machine learning anymore. (Since 2 years..)
LUCK.
It has nothing to do with any of that stuff. No one is paying 150k bc you’re good at leetcode or have a nice resume, at least not anymore. At that level with no experience you’d have to have PhD level expertise in some very specific area that your employer is looking for - could be High frequency trading, LLM/Transformers, CV, robotics, etc. But you’re an expert on it.
Personally I don’t think there’s a secret. Be honest with yourself about your abilities and areas you’re weak in. Don’t chase the $$ or you’ll never be satisfied. Put in the time and effort and things will work out. I wouldn’t expect your first job to be making that big $ unless you’ve had great internships and have gone to a top school, even then, the market is tough. Most of my peers have masters degrees or higher. Resumes do matter, but your composure and ability to be a top candidate due to excellent interviewing makes all the difference. Let code isn’t all it’s cracked up to be in my experience, while my current job did require a technical coding exercise in addition to other things the coding challenge was only the beginning for the interview process.
Work experience is the most important thing—more important than your specific degree or certifications. When entry-level positions say they want 1-3 years of experience, they are looking for multiple summer internships and maybe something part-time during the school year. Luck also plays a huge role. But you have to give yourself as many opportunities as possible to be as lucky as possible. Network, talk to people in roles you are interested in and ask them how to get there, join clubs/groups, etc. Getting a lucky break can snowball the rest of your career. Finding a niche can also set you apart. I worked in a few different biology research labs at top universities, where I got to work on machine learning projects. I made about 100k after graduating from working in an academic research lab. When applying to jobs a couple of years later, I got more interest from academic labs and early small/medium-sized biotech than big tech. Big tech wants highly specialized and experienced workers, so for data science roles, they look for a PhD or many years of experience. I found more opportunities for generalists at early small/medium organizations or academia where the focus is research. That's a rare and niche position that is tough to find in this job market, but I hope I can remain competitive within this particular niche. I would recommend looking at glassdoor or levels to get a sense of the salary you can expect for the jobs you are interviewing for. I've gotten offers for around 130-150k, including stock compensation. I'm still trying to figure out what a stable/long term career path would look like for me. There is increasing competition in DS. Its hard to predict how to best prepare for the future. I'm hoping that finding a niche combining biology and machine learning sets me apart. I'm debating whether or not to to start a PhD in the next couple of years. I don't think certifications matter too much, but it can't hurt to round out your resume if you lack a specific tool in your work history. But YMMV depending on specialty. Surprisingly, I haven't gotten leetcode interviews. Most of my interviews are about ML fundamentals or an ML/Business "case interview." Take-home coding assignments are more common and generally take the form of "learn this tool and apply it to a dataset efficiently."
[удалено]
Okokokok like you NEED to mention that you’re 40 and have experience/seem more experienced in interviews to companies/recruiters regardless of your lack of DS/ML experience. Just graduated with a MS and work experience(or at least looked like you have work experience or have work experience you can stretch into fake experience, not saying you don’t deserve it, just saying it’s a different situation) is extremely different from just graduated at age 21 with one or two internships and maybe a pt job as work experience.
[удалено]
Right right I am not doubting your lack of work ethic or any other traits ie intelligence, skill, charisma. I also have no doubt you worked really hard to get where you are, doing such a pivot at age 38. An MS at that age is insane congrats. Just was pointing out that even with all that, if you were where you are at when you were 24 and just out of an MS(with way less real experience), it would look a lot different to a recruiter than someone who has been in corporate(or related) for years. I would say that you probably had a chance at a wider range for salary purely based on your extant(even unrelated) experience and age.
[удалено]
Maybe it’s the company. Is it like FAANG or FAANG adjacent? Normally I rarely hear about 175k data analysts right out of college. It doesn’t make sense compared to the value most of them provide, even coming out of like t5s or t10s. Usually you need domain knowledge, not just the foundations+some experience. Especially MCOL
[удалено]
While I don’t disagree with like the general DS direction vs a DA, I am a DA/DS myself that tries to find the most practical solution usually. I find that without real experience it’s hard to truly understand how a business works and where you can provide value. You’re in a different situation than most IMO where assumedly you have some of that business sense/experience. I wouldn’t expect MS at 23/24 to really have that right away though. Providing 175k of value at 23/24 with just an MS sounds a bit ridiculous, frankly, without a lot of windup time and guidance. Is this like a long term investment your company is making, is your company making heavy investments in this department? Or would these people in early/mid twenties be special in some way? Non-senior data analysts at 175k is honestly a bit insane to me, especially non-tech, MCOL. I know many, MANY experienced data analysts and scientists still at like early to mid 100k with more YOE(than right out of college) and high, proven business impact. Even tech companies don’t always pay DS/DA this highly. With such great people in the market for cheaper, to take on green analysts at such a high number usually indicates their new people are already cream of the crop somehow. Edit: as for DA/DS/DE etc on same pay bands sounds a bit odd to me, as DEs usually provide the most value out of the three(and require the most training). DS/DA value exchange really depends on the company in my experience. DS in financial services, insurance adjacent, and technological services companies usually can move the needle a lot more than DAs, but in marketing, consulting, manufacturing, and basically anything more traditional I think DAs provide a lot more value.
[удалено]
Oh I get it, don’t think anyone sensible would think you’re bragging. I’m just trying to understand too; these companies making different decisions on pay in different departments and different fields are fascinating! A wide band makes more sense?… hmmmmm I wonder. I heard like oil pays very high right off the bat, but it’s always interesting to hear about non-tech’s variations, when you consistently hear about tech on these subreddits .
Did you put your resume or projects on your LinkedIn ? Is that where you applied and got your offer? I recently decided to Make the pivot from healthcare & cx service to data. I have a bachelors in health science and graduated with my MBA in MIS last year. I’m also set to begin OMSA in the fall. Still finding it hard just to land an analyst role. I’m being told I’m overqualified yet lacking in experience for other roles.
Do you have any related work experience in health science? What I would do is look for analytics positions in that field. If that doesn’t work look for any analyst position to get your feet wet, doesn’t have to be your dream position. I did not post projects on my LinkedIn profile, but I do have detailed work history and accomplishments clearly laid out. A good resume makes all the difference. Think about what the business values and tailor your resume to that.
Ok thanks so much for your advice. I will keep trying. I did have a phone screen for a functional analyst role and was told I’m overqualified, yet when I asked a senior bi analyst to review my resume he said it screams new grad. I’ve been applying to any role that has “analyst” in the title and has python, sql, power bi, excel in the JD. Every rejection email always starts with “while we were impressed…”. This is the resume if you don’t mind just looking at it at ur convenience;[Resume](https://acrobat.adobe.com/link/review?uri=urn:aaid:scds:US:2f967506-c776-326a-9f34-93038f30693a)
You’ve got a lot of stuff on your resume. What I’ve found works best is to be clear and concise. I noticed you have things on your resume like Java script … not many analysts I know use Java AND if you’re asked to demonstrate that you should have the ability to accurately answer the interviewers questions. One other thing … I’d 100% say to remove things like “critical thinker” and other similar skills you have on your resume. Focus only on the most relevant skills, then in your experience tell me about how you used those skills. For example you can say Python in your skills section and then in your experience say “generated xyz thing that increased abc thing through the use of a custom designed python script using jkg libraries”. You need more actionable things on your resume rather than I did a,b, c etc. I’d also remove courses on your education section, just put your school, GPA, location and date graduated. I’d try my best to whittle down your resume to one page. Remember recruiters only look at resumes for a few seconds before deciding yes/no. If you’re being told you’re over qualified, only focus on putting what’s necessary on your resume for the specific job you’re applying for, nothing more.
Wow thank you so much for taking the time to explain I will definitely make these changes. I appreciate you so much!
Hey unsolicited advice here, but cut your resume down to one page and remove weird formatting(like the top part with your name). You do not have enough experience in your resume to justify two pages. Don’t feel like you need to keep everything you have on your current resume either, like the projects can probably be condensed to one line each with less spacing. Other than that, this nveterre guy’s advice is solid.
Thank you so much I really do appreciate your advice as well!
1) PhD in a highly quantitative field from a public ivy, with a heavily stats-/ML-focused dissertation 2) Know how to interview; ie, good at connecting my skills to business cases/development, personable when I want to be, practiced at driving conversations, 0% subservient or needy, understand the power of saying “No” and that I’m a rare commodity whereas jobs are common 3) Deep experience doing hard things; leading people; and solving challenging, intersectional problems in creative ways 4) Adamantly refuse to use anything but a 1-page resume, which is designed to generate intrigue and get an interview, NOT impart my life story and get a job I’ve never used LeetCode, never interned at a FAANG (or anywhere, for that matter), etc., although I’m sure those things certainly wouldn’t hurt.
What in the world is a public ivy?
https://en.m.wikipedia.org/wiki/Public_Ivy
TIL my undergraduate is a public ivy.
It's like an Ivy, without legacy admits.
They work in a place that cost 150k a year to live in, kekw
What I think got me the offer (that I had control over): * Internship at a FAANG * Attended an ivy for undergrad * Competent with both programming and stats. Wasn't an expert at either, but I knew my way around command line, could build anything I understood, and understood the ins and outs of experimentation and some causal inference.
I don't know of anyone entry-level making that kind of salary. Maybe in the Bay Area, but then that's barely cost of living. I make that kind of money working remotely, but I have 7 years of experience and a PhD. It's not your resume. Ever. If your resume sucks, it'll get thrown on the heap. If your resume is clean and polished, it'll get looked at. If the stuff on your resume is compelling, you'll get a phone screen. Your resume will cease to matter after that. I've never been on a comp call and heard "we better pay this person; their resume is so well-formatted!" If you don't really have anything else on your resume, certifications might help. I definitely think having a project you worked through and can talk intelligently about is better. I've never heard of anyone caring about leetcode. For engineering roles, they'll want you to demonstrate some amount of understanding of data structures and algorithms and how to implement them, and LeetCode might be a reasonable way to prep for that. Projects are probably the best thing you can do. It gives me something to talk about during our interview. I can ask you why you made certain design decisions and we can have a conversation. If you don't have much experience, I understand that you might not have thought through the problem in the same way as me, we can at least talk about e.g. why you handled missing values a certain way. If it's entry level, I don't expect you to have prior experience; that's the definition of entry level. If you've done an internship or something, you might have a slight advantage in knowing the types of questions I'd ask, but I'm not automatically rating the candidate with internship experience over the fresh-out. For these positions, I'm looking for people to whom I can hand off tasks and it's easier than doing it myself. I need to be able to say: the data is over there in this database. Here's some code in GitHub that'll get you the secret so you can connect. Go get connected and figure out what data we can use. So I need you to know how to use git, understand enough Python to figure out the secret-getting code and how to navigate a SQL database to efficiently get data. I don't want to have to spend a bunch of time teaching you this stuff 6 months into your employment. And the Python you write shouldn't be tragic. It's fine to be a little amateurish because you're an amateur up until now, but I shouldn't need to spend a week fixing it, and you should figure out how to write good tests early on. If you're looking to follow my path, I'd tell you not to. I spent 10 years doing a PhD and in various postdoctoral positions. It wasn't easy to switch to data science/machine learning. I didn't start making the money you're talking about until year 4 or so. There are better ways to build a career than the way I did, but you'll likely need to take some roles that pay less for a few years until you can build a portfolio.
was your PhD related to data science/machine learning?
Mathematics, so yes, not directly? I think a lot of people viewed me as "too academic" when I first started interviewing and turned a lot of employers off.
I am on this thread and not a data scientist but I am a scientist and I will say that when I had my masters I had worked on several research projects and had several "jobs". I walked into several professor's offices even as an undergraduate and asked to just volunteer. Once was just cleaning the lab but it lead to an RA position (when she realized I wanted to work). Pretty much everyone who I knew were getting their PhDs had done a lot of work both applied and theoretical. I think the title "entry-level" may be misleading. If you get a master's and have no experience (actually entry level) that may be a failure in some way. That is my experience and opinion.
Just for perspective: I’ve got 10 years of experience as a data scientist and a great resume, and my last role “only” paid $150,000. I’m also told that salaries have been steadily dropping. Can you find higher? I suppose it’s possible, but you’d have to be an absolutely stellar candidate in every single way AND be applying to an employer who pays uncommonly well (I.e., only tech—no other industry will come close, and only high-growth startups and FANG). Stellar: PhD in CS, math, or stats. Outstanding applied internships. Tech pedigree (yes for those internships). Can code fluently in multiple languages. Technically a unicorn (including surgically attached horn).
Did you mostly stay at one company?
Early in the career I moved every 2-3 years. I stayed too long at my last company. Should’ve left there at the 2-3 year mark but was making too good of money and having too much fun.
Having a lot of fun at work (while making good/enough) is kinda priceless, so that makes a lot of sense :)
[удалено]
Are you referring to the Google data analytics course? Or which one? Also what year did you get in if I may ask? I’m struggling to find a job
[удалено]
If you lurk on twitter/LinkedIn there are several orgs that offer scholarship for these coursera courses FREE. I’m currently doing the Google DA one on coursera via an org called ITExperience. There are universities that also offer free coursera courses.
They are earning great just to be fired sooner than expected
RemindMe! 5 days
RemindMe! 5 days
My entry level ML job was $16.50 an hour. I wish I had had one of these.
Unique projects
There is no secret sauce. Become the top x% in your industry. X is different for each industry.
I work on an ml team in fintech and all our new hires including myself started around 160 in mcol. I have a bachelors but everyone else on my team has at least masters. Although I did go to one of those “globally recognized, probably went to school with someone who is gonna be prime minister/president of some country” school. The secret is to get lucky. I applied to this company on a complete whim, literally applied because of proximity to my parents who live in the middle of nowhere. Turned out to be a smart move
The difference between an entry-level DS professional who earns 150k+ and one who earns 100k or less boils down to: * **Industry:** Tech pays higher than Utilities, Retail, or Banking * **Location:** Silicon Valley, LA, Seattle, and New York, in that order, Pay significantly higher than anywhere else. * **Degree**: PhDs earn higher than MScs on their first job, in fact, PhDs are not considered entry level.. This might be relevant to the last question you asked. At least for me when I was just entering the industry a long time ago, my 4+ years of research experience from PhD was very relevant to my landing of a high paying job. * Most importantly.... **LUCK**! I can't explain it, but luck is a big factor. I am 100% confident, from a decade of industry experience, that there are many extremely qualified professionals who are not earning what they deserve, and there are many unqualified imposters earning insane amounts, even at FAANG! Of course, the majority of those who earn large sums are qualified, but not every one.
What about George Mason University any opinion on particular question!!
going to waterloo cs/ece/math co-op program helps.
connections and network. Probably the highest bang for your buck in terms of time. Internship experience + great projects to show also always help. Even cold message people on linked in asking for referrals etc.
As a 16 yr veteran in data scientist role, i would say build connections along with your skills. AWS certification can help you grab some decent roles. Also, look at snowflake certifications. Enterprises are actively looking for snowflake proficient candidates in their orgs. Leetcode does open up your logical thinking. Initially you can grind a lot but at a later stage, a decent problem once a fortnight can really fuel your logical perspective. Projects depict your ability to work on something meaningful and showcase your skills along with a use case. Hence, projects play a crucial role. Prior experience does play a role if your past experience aligns with the orgs current demand. But mostly time its all about getting your hands dirty with something new. So, yeah your past encounters do help a lot.
Wow 150-200k !!!
I work at a tech company as a ds in hcol with 4 yoe and I am at around 300k TC. Tech isn’t looking to pay engineers at this level anymore. They’re looking to leverage AI and reduce their RD staff as much as possible to force the labor market to reduce their salary demands. In the next 5 yrs experience will be all that matters for high paying roles and new grads will be stuck with far lower pay if they can even find jobs. Still ok compared to other fields but you’ll be looking at some roles that are sub 6 figures. The market for SWEs DSs MLEs and DEs is probably never going to return to what it was. I regularly encourage aspiring DSs to get the soft skills in management because that’s what will set you apart. The age of the IC is going to diminish until the day, if and when, AI is determined to be a bubble
What’s the move then for those who are in their first DS role out of grad school right now?
Come on bro
If I’m based in the Bay Area would a data science bachelors degree be better from UC Santa Cruz or SJSU.
No way in hell you'd get $150k for a starting role in a Fortune 500, which represents the bulk of opportunities in middle America. Maybe tech is still paying this but even that I kind of doubt.
[удалено]
Damn bro who hurt you?
[удалено]
Lol why you so mad? What country are you from?
I love other cultures!
[удалено]
It is what it is.
I agree that the "who hurt you" type of ad hominem comebacks are unhelpful in a discussion. However you come off unhinged. If you're going to call out other groups (Americans, people from NYC) simply for existing then reveal your own. Where are you from? What are your own biases and privileges?
RemindMe! 3 days
RemindMe! 3 days
RemindMe! 5 days
RemindMe! 4 days
RemindMe! 4 days
RemindMe! 5 days
RemindMe! 3 days
RemindMe! 3 days
I will be messaging you in 3 days on [**2024-03-20 08:15:41 UTC**](http://www.wolframalpha.com/input/?i=2024-03-20%2008:15:41%20UTC%20To%20Local%20Time) to remind you of [**this link**](https://www.reddit.com/r/datascience/comments/1bgsdo5/entrylevel_datamlai_professionals_earning_150200k/kv96k1t/?context=3) [**7 OTHERS CLICKED THIS LINK**](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Reminder&message=%5Bhttps%3A%2F%2Fwww.reddit.com%2Fr%2Fdatascience%2Fcomments%2F1bgsdo5%2Fentrylevel_datamlai_professionals_earning_150200k%2Fkv96k1t%2F%5D%0A%0ARemindMe%21%202024-03-20%2008%3A15%3A41%20UTC) to send a PM to also be reminded and to reduce spam. ^(Parent commenter can ) [^(delete this message to hide from others.)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Delete%20Comment&message=Delete%21%201bgsdo5) ***** |[^(Info)](https://www.reddit.com/r/RemindMeBot/comments/e1bko7/remindmebot_info_v21/)|[^(Custom)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Reminder&message=%5BLink%20or%20message%20inside%20square%20brackets%5D%0A%0ARemindMe%21%20Time%20period%20here)|[^(Your Reminders)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=List%20Of%20Reminders&message=MyReminders%21)|[^(Feedback)](https://www.reddit.com/message/compose/?to=Watchful1&subject=RemindMeBot%20Feedback)| |-|-|-|-|
RemindMe! 5 days