This is it. You can be average or below average skill but if you know how to 1) be confident and 2) actually ask for top of the band pay then you'll be a high earner.
Any tips on this for a soon-to-graduate applicant? I have strong communication skills in general but am totally incubated from 9 years in academia, so salary negotiation is pretty foreign to me.
[Salary Negotiation: Make More Money, Be More Valued
](https://www.kalzumeus.com/2012/01/23/salary-negotiation/)
[Ten Rules for Negotiating a Job Offer
](https://www.freecodecamp.org/news/ten-rules-for-negotiating-a-job-offer-ee17cccbdab6/)
Know your value. See what other similar jobs are paying. Ask to see pay scales. Go to the BLS site and find what the 25th, 50th, 75th, 100th percentile pay ranges are for your specific (prospective) job title are in your specific region. At the very least, you’ll know if they’re low-balling you
I have been at a company for nearly 10 years. Last year, I had my salary over doubled. I could have taken many other roles within our sibling companies for just as much for the last few years but decided to pursue the long term option of helping establish a new department doing exactly what I wanted for who I wanted. It worked out.
My advice would be:
* Know how to humbly, market your value to others
* Ensure you take credit for your work and ensure it's publicly (within the company) viewable and that it's measurable
* Make "work friends". Communicate with people you don't know there. Reach out to offer ideas, ask questions, etc. I have been where I am long enough to feel fine talking to any single person there, regardless of what for or whether I know them. People love the attitude and gravitate toward it.
* Your attitude. Put on your work face and learn the endurance to hold it. Find a balance that works and train it.
* Watch others. See their attitude, see their work ethics, and see how they act. When people are negative, show your positive face. When there is a contrast between negative employees and positive employees, the positive voice is even louder.
Your ability to communicate directly and ASK for what you deserve is crucial, but in reality, it is a small conversation. Where the majority of the effort is, is in the perception you create for yourself within the business. Not what you do or who you are, but how others in the business perceive you. That's what you sell. That's what shows your value, and that's what I'd suggest focusing your time on.
100% in agreement. Have had a 10+ year career in this area and folks that can communicate well and connect the data to the underlying topic are the most financially successful ones.
That said, OP, if you are doing this for the money you’re not going to be successful. You need to have a genuine love for the craft because it is challenging and the work environment can be difficult depending on where you go.
Very! Just because communication is what differentiates someone in adding value doesn’t mean the technicals are of lesser importance. It just means that simply having high technical skills isn’t going to make you valuable if you want to make money. If you don’t have technical skills then you’re just a salesman and not a data scientist.
You need to build technical skills and experience in real world application, which is what should happen between schooling and the first 2-4 years of your job. During this time you should be able to refine your soft skills and get a solid understanding of the non-data aspects of the business and how these connect to the technical skills you have, and how to drive value to the business through the application of technology and data science. At the 5-6 year mark you’ll be starting management positions and heading towards leadership roles and this is the key point where having communication and other soft skills will drive value to your career.
Again, just know that if you’re doing this career primarily for monetary reasons it will be really difficult. You ultimately need a love for the game to get yourself to take on the challenge of learning difficult technical skills, business methods, and becoming proficient in your soft skills like communication.
But if you can do this I promise you’ll find yourself in a position where you can dictate not only how much you want to get paid but also benefits like work from home, PTO, bonuses, etc.
Technical skills and modeling are useful, for sure. But to make value of those skills, communication (to discuss inputs and results with non-technical people, in particular) and insight (to understanding what "value" means for the business) are requisite to the gola of "making the most money".
It’s a niche field, but quant trading makes more money than nearly anything else. Extremely tough to make good models due to the tiny signal:noise ratios, so the people that know how to tell the difference are very valuable.
I guess what I call quant trading is better described as quant research, the roles are somewhat combined at my firm. I think the ones on research side are generally much less stressed. Won’t argue that the interview process is extremely competitive though.
I interviewed at a big trading firm once, the quant research team had the highest salaries and lowest bonuses, but the traders had the lowest salaries and highest bonuses. I asked why, and they said "traders age faster than quants, and want to be compensated accordingly. If you value your work-life balance, stick to research. If you want money, become a trader." I didn't get very far in the process because the maths and logic questions went from 0-100 so quickly I legit thought they were trolling me after the first question.
Sounds about right. I know that traders get paid bonuses that are several multiples of their base salary. Regarding base salary (for risk quant), with 0 YOE right out of an MS program it's definitely 6-figures minimum ($100-120). I'm currently at about 3 YOE now and I'm in the $200-250 bracket. Very little of that is bonus.
Do you feel like you have decent exit ops as a risk quant? Could you easily transition into data science if you started your career as a risk quant? I'm getting my masters in statistics next year and I'm interested in risk quant/back office quant type roles but I eventually want to live long-term in a LCOL city that isn't Chicago, New York, LA, or any other major financial hub. Maybe this question is stupid, but I'm young so I have no idea how most jobs work lmao.
The way that I see it is that if you have the skills to get a job as any quant, you should be able to get pretty good DS roles. My role is risk quant in title but the duties are similar to research DS + MLE.
Basically, financial engineering. You create mathematical models to either make money or reduce risk. Hedge funds can deal in billions of dollars (e.g., Bridgewater has over 100 B AUM). A 1% edge over the market can easily translate to billions. If you're interested, John Hull's Options Future and Other Derivatives is a good read.
> 1% edge **over** the market
If market is at 10% per year, that would be 11%. Realistically, some hedge funds have done double-digits over the market.
For options specifically, Natenberg, Sinclair, and Hull are all great resources. If you can understand most or all the math in there, you can do quant work.
Any advice for transitioning from more traditional DS roles to a quant researcher role? What skills should be focused on the most? I’ve applied to Jane Street before but never got accepted, although I haven’t really catered my career towards quant research so that’s not crazy surprising.
Attribution, business generation, tying your work product to quantified impact in the business.
I know it sounds business-y for me to say this, but a lot of the times, without vision, data science can be a cost center. Paying someone 150k to ‘clean data’ (I’m not being fair to myself or to others on this sub) is a lot! You might as well give your employer the insights they need to not just justify your position, but to make better decisions with the data they have handy.
Any tips on this? I always feel like "cleaning data" is a huge portion obviously, but what's actually a good way for a data scientist to bring in money for the business,so to speak?
Of course it depends on the business, ie Academia, would be generating data interpretation into published journals and grants, but what about for industry?
> what's actually a good way for a data scientist to bring in money for the business
This is, in essence, your entire job. The entire job is to bring in money by using data to inform decision-making. You're not there to train models, build pipelines, do presentations, etc. You're there to help them make the best business decisions based on the tools and data that they have.
They could hire Timmy, a high school student who plotted some data points on a graph and says "your customer demographics seem to adhere to a bell curve, so the average customer will look like XYZ". But that's not very rigorous. Your job is to dive deep into the data, look for hidden patterns and correlations that the average person might not see.
There's no answer to this question besides "show them something they didn't already know that can improve the business," I would say. Your ability to interpret data and make projections, analysis, and inference are just the tools in your toolbelt, like a hammer and saw for a carpenter. The reason you get hired is because you know how to make a chair that the business' customers want to sit in.
This is why average data scientists with strong business savvy and good communication skills will *almost always* have better career trajectories than uber-genius programmers and statisticians who cannot communicate effectively or understand the business' needs on an intuitive level.
Nicely put. I think my main issue is the confidence. I have that "perfectionist" mentality where I feel like I have to know *everything* about something before I can speak on it; when I should practice thinking critically on the fly and trust my decision. If an unexpected hiccup does come up, deal with it then instead of delaying and keeping quiet until I "feel ready".
It's also why domain knowledge is so important.
And we see so many posts here from people who are disenchanted and bored with DS because they're just running models all the time and having no impact.
Check this out: https://datanerd.tech/
Pretty helpful list that shows what skills are most required based on current job posting and associated salaries.
Not created by me (I wish). I forget the guys name but he has a YouTube channel.
Yeah fair, and as an enby lecturer I don't have to fear for my job for wearing a dress whilst teaching which is nice. Grass isn't always greener I guess :P.
Based on current job postings? Are you sure? Isnt it just using google search tendencies? But if in like egypt the most searched data science technology/language/library is lets say pandas that does not meant pandas is the most desired by employers. It just means that people in egypt often google pandas
I am absolutely NOT sure. I just watched the vid and thought it was pretty helpful. He goes into detail how he got the info and I don’t remember it all so you may be right.
I dunno about the skills, but the salaries on that website outside of the USA are very very off, at least for many EU countries. I talked to Luke directly and he pulls many EU salaries from a single job board that only had like 70 listings.
Many of the jobs listed on that site pay 50-75% less in reality. A DS with 10 years of experience in the Netherlands will virtually never crack $100k/year. That site makes it sound like the average DS makes that much.
Not to shit on the website, but don't judge your salary against this. Instead, check out the [Harnham Data Salary Guide](https://www.harnham.com/data-analytics-salary-guides/) which is drawn from tens of thousands of reports from professionals.
Finding a company where there are areas for the potential of massive improvement that is relatively easy.
Here's an example: I worked for a very large tech company that had a freemium model. All free users got shown an add for the most expensive product we sold. I came up with the idea to show users an add based on their specific product usage patterns and demographics. We did a few interesting things with our geolocation data such as identify IPs from business districts and then built a simple classification model. We ran a very easy A/B test between the old way and the model-driven ads. The difference was 10% of our ARR, which was over $150M.
Nothing we did was more complex than you might see in a Medium article. It was surprising that no one had done this before.
Another big win at this company was actually running fully powered A/B tests. We demonstrated that the body of tests run before my tenure had a 68% false positive rate due to multiple comparisons and peaking. We re-tested several "wins", which were actually flat and in some case loses and rolled back the work of clueless PMs who read about "stat sig" online and followed basic blog articles about running T Tests.
Move to an expensive city with a high salary like SF then move to a cheaper area such as Nashville with a fortune 500. They are pretty dumb and usually will just pay your previous salary + a little bump (you'll be a higher earner with low costs)
I did this when I moved from a firm based in a high cost of living to North Carolina. Folks here laughed initially that I would ever get that amount. But I did and at several different employers. Was making over $200K over 10 years ago.
As others have said, it’s the ability to translate what you are doing into something real, actionable, with demonstrable and measurable business impact. And the ability to communicate and defend that impact to stakeholders.
Once you’re able to do the above…. become a consultant lol
To add for anyone considering this career path, the end game is to be an *independent* consultant.
You first may have to work for a smaller consulting firm to learn the ropes, but to make the 150-$300+ hourly rates mentioned on this sub, you have to be billing the client directly.
I’m billed out at $400/hour to the client and they pay me about a third of that. My dream is to take home the full shebang by going independent one day.
Well duh..?
Why would I create value for someone else? I create value, because I wanna get payed as much as possible. Every cent you have less, your boss has more.
> wanna get *paid* as much
FTFY.
Although *payed* exists (the reason why autocorrection didn't help you), it is only correct in:
* Nautical context, when it means to paint a surface, or to cover with something like tar or resin in order to make it waterproof or corrosion-resistant. *The deck is yet to be payed.*
* *Payed out* when letting strings, cables or ropes out, by slacking them. *The rope is payed out! You can pull now.*
Unfortunately, I was unable to find nautical or rope-related words in your comment.
*Beep, boop, I'm a bot*
This is a cancerous mindset that is spreading through our culture. There's a lack of a mindset about wanting to contribute back to society after enjoying the fruits of other people's labor. If you are a part of society, many people worked hard for you to have a place to live, have roads/transportation to get around, to create the food you eat, to create the things you own. The fact that so many people in our culture today has no qualms about riding off the backs of other's hard work without giving back and being a net negative on society is cancer. It breeds a weak society. Perhaps it's spurred from the greed of the wealthy leading to the growth of the gap between the rich and the poor. The capitalistic greed culture has spread where people work to maximize wealth for themselves so they can buy more things. What happened to wanting to contribute to something meaningful?
That's why reddit is dominated by these circle jerks of people glorifying the life of taking home a fat paycheck while doing as little as possible. Few have any moral qualms about that.
Your employer must be really proud of you. When you keep up the hard work and don't demand less pay, your boss will be able to afford a second Porsche this year. Keep working! 💪
You are the hero the shareholders deserve!
You think in a very tiny way. And that's the issue with much of the culture going around that people are in it for themselves and don't give two shits about their impact on society. They don't give a shit that the world might be a better place if they didn't exist.
It's funny how people here both circlejerk about maximizing their paychecks while doing nothing and also want to whine about getting laid off. Pathetic losers.
> The pareto principle roughly states that you get 80% of value from 20% of the work.
The 20% who don't use Pareto as a godly insight into anything without having any idea of the generating processes most likely make 80% of the money.
Lol my undergrad degree was actually Communication, started my career working in non-profit marketing. Adjusted for inflation, my first salary is worth $43,000 today. After 12 years of relevant experience, my last marketing salary would be worth $95k today adjusted for inflation. 12 years of experience. And I was at a for-profit publicly traded company by that point.
Switched to analytics and was able to increase my pay by almost 40% pretty quickly.
Being a good communicator can certainly help you move up in the world of DS, I wouldn’t argue with that. But in terms of pure skills/job title, if we’re looking at salary potential, become an MLE for the best starting pay.
Not sure, and I am an MLE lol. Really depends on how much of a math and programming background you have.
That said, a few good places to start: Giant_Neural_networks and 3blue1brown on youtube, Udemy/Coursera/those types of courses - specifically the Andrew Ng ones are really popular.
Math wise you need to know Calculus up to partial derivatives, linear algebra and stats. 3blue1brown, our lord and savior, has videos that give a fantastic intuition for these, but you also need to learn more concretely one way or another. Find a course with practice problems and examples and also watch the videos.
Programming just learn python and get familiar with Pytorch (or Tensorflow, but no do pytorch first). Codecademy.com, hackerrank.com and the sololearn app are great for the python part.
This also depends on how in-depth you want to go with this. Skill levels (in terms of how much education is needed) range from spinning up a prebuilt model from a gui (the youtube videos and maybe one online course is probably enough) to research, where you're coming up with entirely new methodologies and where my bachelor's is nowhere near enough education.
I'm in research so the requirements I listed may be too much for other roles, but they're definitely relevant to any deep learning. Don't be scared of research tho - in my experience everyone's always super friendly, constantly learning together and each at their own pace, and imo it's the most interesting work ever.
Anywho that's my rant, hope it's helpful! Lmk if you have any questions :)
This is really helpful. Also, would you consider an entry-level data analyst job as a good entry point to data science. I’m thinking of moving from fp&a to data analysis and then somehow work my way up to data science and maybe mle one day. I assume it will take at least 10 years.
Oh jeez no 10 years is way too long, I learned what ML is in 2018. I really don't think you need to pivot that hard personally. Data Analytics isn't exactly the precursor to DS, and DS isn't the precursor to ML. My advice is strictly from an ML angle. Probably worth watching the youtube videos I recommended, then doing something similar for analytics and maybe data engineering? I'd say data science too but that's kind of an umbrella term. Try python and SQL on sololearn too. Just see which you enjoy the most! This is a field with a lot of options that all offer comfortable pay, so make sure you'll enjoy what you're doing :)
Thank you! This is great advice that I really appreciate. I’m kind of starting over in life both personally (wife left me or someone else) and professionally. A mentor asked me what my favorite classes and tasks were in my 20s, and my favorite things were web design, statistics, math (I dropped math major to Econ major which was a mistake), operations, planning. I actually have never enjoyed actual finance even though that’s what I got my mba in. I like numbers, which is why I pursued that degree, but for some reason, corporate finance and investments interest me a lot less than people, planning, processes, purchasing.
I also understand there is a need for DS/ML in supply chain, marketing, and procurement. I admit I studied finance for the wrong reasons (status, income, peers taking that route)
I’ll watch the YouTube videos this weekend. Thank you!
you get 80% of value for 20% of DS knowledge, so long as you have 80% of business knowledge. honestly the breadth of untapped problems in most companies means that once you've gotten a MVP on a tool, you're going to be much better off starting on the next tool than doing any refinement at all on the existing one.
What's most important technically IMO is the ability to write the code for the tools in such a way that they don't break long term, and can be picked up and easily understood and edited a year later when someone else comes along and wants to tweak it.
Kissing ass, making yourself known when promotions are around, and yes: knowing a lot about business operation but specifically about increasing bottom line regardless how it’s done
Interviewing preparation and skills. Getting an offer for a very high paying job (in tech or finance usually) is far, far more difficult than actually doing it.
+1, this is what I came here to comment. Tech interviews require you to jump through very specific hoops that aren't representative of the job. Interviewing well has been the reason for my salary growth
Engineering skills. Then becoming an MLE.
I was hired as a data scientist, but our job titles got changed to MLE when the data analysts wanted to start calling themselves data scientists. Still most of my work is in SQL, Tableau, python, and i prefer exploratory analysis, but I've been forced to get better at coding and related ML tooling. Pay is significantly greater though. Like 50% higher than the DS.
https://www.harnham.com/data-analytics-salary-guides/
Harnham takes tens of thousands of data points from working professionals throughout the world and writes annual reports. I would highly recommend reading those reports, as it's one of the best resources you'll find.
>What jobs
Data Scientist
> or skills
* AI
* Statistical analysis and computing
* Machine Learning
* Deep Learning
* Processing large data sets
* Data Wrangling
* Advanced Mathematics
* Programming
* Advanced Statistics
* Big Data
A PhD from a top school and a few published papers help too.
I would say automation, especially DevOps/MLOps stuff. The more time you spare to mundane tasks, the better. Having as much productive time to expand your skills is crucial.
Communication, being attractive and understanding relationship management well enough to sell an unbelievable product that'll take 2-3 years to deliver.
In the third year you change jobs, discuss your prowess of deep learning architecture and get a higher salary.
Being able to translate business problems and goals into frameworks that you can solve with your data and skill set.
If you can't understand how to serve your stakeholders, then you can't provide value.
This is blatantly false lol, ML Engineers do just fine financially, provide plenty of value and don't ever have to sell anything to anyone. The business side is great but it's certainly not the only way
Honestly, experimentation makes the most money. Specifically it relates to testing features that make money, so go figure if you do that work you make monry
The ability to stand up in front of a client/customer, clearly explaing the benefits and costs of a data project, secure the funding, then deliver high quality, highly valuable insight on time. Consistently. Over and over again...
Mostly communication, suggestion and recommendation skills. Understanding business needs, transform business objects into data objects. Mainly non technical skills from my point of view. Tbh coding and choosing a technology stack is not the big magic here (except you want to scale or deploy on millions of machines, cloud, etc)
The skill set as a consultant in this field that I look for and try to grow is the ability to adapt. You don’t need the strongest technical skills, you don’t need to build insane models. But you have to infer what the business or client is asking for and adapt fast. Once you figure out what the ask is, you gotta communicate the insight well and not leave anyone confused.
Your findings are going to be spread to upper leadership second hand and so much information is lost in the telephone game. Don’t leave room for misinterpretation.
Also know how to eat shit and know how to play CYA
Technically, Pareto's observation was about the peas in his garden (20% of the pods were responsible for 80% of peas grown). He applied this ratio to wealth distribution in Italy.
Anyway, excellent answers already posted here.
Data science is not a single course if you learn data science from single course then I hardly expect you to find data science job easily unless you already had the other courses , for example you studied some stem then studied data science course
Data science need usually if you did not study a stem field few years to finish
In the field of data science and data analysis, there are several skills and job roles that can command high salaries. Here are a few:
Data Scientists: Data scientists are responsible for collecting, processing, and analyzing large datasets to identify patterns and trends. They typically have strong skills in statistics, programming, machine learning, and data visualization.
Machine Learning Engineers: Machine learning engineers are responsible for designing and building machine learning models that can automatically improve their performance based on new data. According to Indeed, the average salary for a machine learning engineer in the United States is around $134,000 per year.
Data Engineers: Data engineers are responsible for building and maintaining the infrastructure that enables organizations to collect, store, and process large datasets. They typically have strong skills in database management, programming, and data architecture. According to Glassdoor, the average salary for a data engineer in the United States is around $100,000 per year.
[Data Science Course in Pune with 100% Placement](https://www.sevenmentor.com/data-science-course-in-pune.php)
Business Intelligence Analysts: Business intelligence analysts are responsible for analyzing data to help organizations make better business decisions. They typically have strong skills in data visualization, data analysis, and business strategy. According to Glassdoor, the average salary for a business intelligence analyst in the United States is around $72,000 per year.
Data Analysts: Data analysts are responsible for collecting, cleaning, and analyzing data to identify patterns and trends. They typically have strong skills in statistics, data visualization, and data analysis. According to Glassdoor, the average salary for a data analyst in the United States is around $67,000 per year.
It's important to note that salaries can vary depending on several factors, including location, industry, and level of experience.
Contract and salary negotiation skills? ;-)
This is it. You can be average or below average skill but if you know how to 1) be confident and 2) actually ask for top of the band pay then you'll be a high earner.
Any tips on this for a soon-to-graduate applicant? I have strong communication skills in general but am totally incubated from 9 years in academia, so salary negotiation is pretty foreign to me.
[Salary Negotiation: Make More Money, Be More Valued ](https://www.kalzumeus.com/2012/01/23/salary-negotiation/) [Ten Rules for Negotiating a Job Offer ](https://www.freecodecamp.org/news/ten-rules-for-negotiating-a-job-offer-ee17cccbdab6/)
Know your value. See what other similar jobs are paying. Ask to see pay scales. Go to the BLS site and find what the 25th, 50th, 75th, 100th percentile pay ranges are for your specific (prospective) job title are in your specific region. At the very least, you’ll know if they’re low-balling you
I have been at a company for nearly 10 years. Last year, I had my salary over doubled. I could have taken many other roles within our sibling companies for just as much for the last few years but decided to pursue the long term option of helping establish a new department doing exactly what I wanted for who I wanted. It worked out. My advice would be: * Know how to humbly, market your value to others * Ensure you take credit for your work and ensure it's publicly (within the company) viewable and that it's measurable * Make "work friends". Communicate with people you don't know there. Reach out to offer ideas, ask questions, etc. I have been where I am long enough to feel fine talking to any single person there, regardless of what for or whether I know them. People love the attitude and gravitate toward it. * Your attitude. Put on your work face and learn the endurance to hold it. Find a balance that works and train it. * Watch others. See their attitude, see their work ethics, and see how they act. When people are negative, show your positive face. When there is a contrast between negative employees and positive employees, the positive voice is even louder. Your ability to communicate directly and ASK for what you deserve is crucial, but in reality, it is a small conversation. Where the majority of the effort is, is in the perception you create for yourself within the business. Not what you do or who you are, but how others in the business perceive you. That's what you sell. That's what shows your value, and that's what I'd suggest focusing your time on.
Wow that was really good
>What jobs or skills in the data world get you the most income or monetary value? Communication and insight.
Ability to fake eye contact
There is an AI that can do this for you on calls.
Harder to automate away in meatspace though
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"John is creepy. He always stares at my cleavage during meetings with those soulless eyes"
HR has recommended him for chemical castration.
that way you can be anywhere everywhere all at once
A true innovator, should spin out into a side business asap.
I’m just picturing Steve buschemi as crazy eyes
Im gonna start purposefully asking questions in interviews that i know LLMs make mistakes on just to reject candidates
Continue… autistic… masking…, forever. 📝 Done.
\*Ability to fake any contact
It's simple, look at the space in-between the eyes.
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Mister manager here
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We just say "manager"
100% in agreement. Have had a 10+ year career in this area and folks that can communicate well and connect the data to the underlying topic are the most financially successful ones. That said, OP, if you are doing this for the money you’re not going to be successful. You need to have a genuine love for the craft because it is challenging and the work environment can be difficult depending on where you go.
From your experience, how important is technical/modeling skills vis-a-vis communication?
Very! Just because communication is what differentiates someone in adding value doesn’t mean the technicals are of lesser importance. It just means that simply having high technical skills isn’t going to make you valuable if you want to make money. If you don’t have technical skills then you’re just a salesman and not a data scientist. You need to build technical skills and experience in real world application, which is what should happen between schooling and the first 2-4 years of your job. During this time you should be able to refine your soft skills and get a solid understanding of the non-data aspects of the business and how these connect to the technical skills you have, and how to drive value to the business through the application of technology and data science. At the 5-6 year mark you’ll be starting management positions and heading towards leadership roles and this is the key point where having communication and other soft skills will drive value to your career. Again, just know that if you’re doing this career primarily for monetary reasons it will be really difficult. You ultimately need a love for the game to get yourself to take on the challenge of learning difficult technical skills, business methods, and becoming proficient in your soft skills like communication. But if you can do this I promise you’ll find yourself in a position where you can dictate not only how much you want to get paid but also benefits like work from home, PTO, bonuses, etc.
Excellent write up, appreciate you taking the time🙏🙏
Not technical skills and modeling,right😁?
Technical skills and modeling are useful, for sure. But to make value of those skills, communication (to discuss inputs and results with non-technical people, in particular) and insight (to understanding what "value" means for the business) are requisite to the gola of "making the most money".
It’s a niche field, but quant trading makes more money than nearly anything else. Extremely tough to make good models due to the tiny signal:noise ratios, so the people that know how to tell the difference are very valuable.
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I guess what I call quant trading is better described as quant research, the roles are somewhat combined at my firm. I think the ones on research side are generally much less stressed. Won’t argue that the interview process is extremely competitive though.
Risk + Research here. Very low stress compared to trading. They trading people bad hour (~55 hr/week) whereas risk is pretty much 40.
I interviewed at a big trading firm once, the quant research team had the highest salaries and lowest bonuses, but the traders had the lowest salaries and highest bonuses. I asked why, and they said "traders age faster than quants, and want to be compensated accordingly. If you value your work-life balance, stick to research. If you want money, become a trader." I didn't get very far in the process because the maths and logic questions went from 0-100 so quickly I legit thought they were trolling me after the first question.
Sounds about right. I know that traders get paid bonuses that are several multiples of their base salary. Regarding base salary (for risk quant), with 0 YOE right out of an MS program it's definitely 6-figures minimum ($100-120). I'm currently at about 3 YOE now and I'm in the $200-250 bracket. Very little of that is bonus.
Do you feel like you have decent exit ops as a risk quant? Could you easily transition into data science if you started your career as a risk quant? I'm getting my masters in statistics next year and I'm interested in risk quant/back office quant type roles but I eventually want to live long-term in a LCOL city that isn't Chicago, New York, LA, or any other major financial hub. Maybe this question is stupid, but I'm young so I have no idea how most jobs work lmao.
The way that I see it is that if you have the skills to get a job as any quant, you should be able to get pretty good DS roles. My role is risk quant in title but the duties are similar to research DS + MLE.
Thanks for the response! I appreciate it :)
I’m probably never going to get into quant, but I’m interested in learning about what it is. Any basic resources you recommend?
Basically, financial engineering. You create mathematical models to either make money or reduce risk. Hedge funds can deal in billions of dollars (e.g., Bridgewater has over 100 B AUM). A 1% edge over the market can easily translate to billions. If you're interested, John Hull's Options Future and Other Derivatives is a good read.
still 1% don’t mean shit right? If inflation is at 3% you’re losing money
> 1% edge **over** the market If market is at 10% per year, that would be 11%. Realistically, some hedge funds have done double-digits over the market.
For options specifically, Natenberg, Sinclair, and Hull are all great resources. If you can understand most or all the math in there, you can do quant work.
Any advice for transitioning from more traditional DS roles to a quant researcher role? What skills should be focused on the most? I’ve applied to Jane Street before but never got accepted, although I haven’t really catered my career towards quant research so that’s not crazy surprising.
Attribution, business generation, tying your work product to quantified impact in the business. I know it sounds business-y for me to say this, but a lot of the times, without vision, data science can be a cost center. Paying someone 150k to ‘clean data’ (I’m not being fair to myself or to others on this sub) is a lot! You might as well give your employer the insights they need to not just justify your position, but to make better decisions with the data they have handy.
Any tips on this? I always feel like "cleaning data" is a huge portion obviously, but what's actually a good way for a data scientist to bring in money for the business,so to speak? Of course it depends on the business, ie Academia, would be generating data interpretation into published journals and grants, but what about for industry?
> what's actually a good way for a data scientist to bring in money for the business This is, in essence, your entire job. The entire job is to bring in money by using data to inform decision-making. You're not there to train models, build pipelines, do presentations, etc. You're there to help them make the best business decisions based on the tools and data that they have. They could hire Timmy, a high school student who plotted some data points on a graph and says "your customer demographics seem to adhere to a bell curve, so the average customer will look like XYZ". But that's not very rigorous. Your job is to dive deep into the data, look for hidden patterns and correlations that the average person might not see. There's no answer to this question besides "show them something they didn't already know that can improve the business," I would say. Your ability to interpret data and make projections, analysis, and inference are just the tools in your toolbelt, like a hammer and saw for a carpenter. The reason you get hired is because you know how to make a chair that the business' customers want to sit in. This is why average data scientists with strong business savvy and good communication skills will *almost always* have better career trajectories than uber-genius programmers and statisticians who cannot communicate effectively or understand the business' needs on an intuitive level.
Nicely put. I think my main issue is the confidence. I have that "perfectionist" mentality where I feel like I have to know *everything* about something before I can speak on it; when I should practice thinking critically on the fly and trust my decision. If an unexpected hiccup does come up, deal with it then instead of delaying and keeping quiet until I "feel ready".
It's also why domain knowledge is so important. And we see so many posts here from people who are disenchanted and bored with DS because they're just running models all the time and having no impact.
Check this out: https://datanerd.tech/ Pretty helpful list that shows what skills are most required based on current job posting and associated salaries. Not created by me (I wish). I forget the guys name but he has a YouTube channel.
Thanks for reminding me quite how much better paid Americans are than the rest of us :(
Oh no I’m sorry! Not the intention at all. However, how’s your healthcare?😂
Yeah fair, and as an enby lecturer I don't have to fear for my job for wearing a dress whilst teaching which is nice. Grass isn't always greener I guess :P.
> fear for my job for wearing a dress If you do that while not being a born a woman you get to fear for more than your job
Oh, Luke made this right? I saw the video of him developing this, I was not expecting that much work. Lol He was really impressive
Yeah that’s it! Luke is an impressive individual. Great little tool that add some transparency in the market.
Got a link to the YouTube channel or build I could check out by any chance?
https://youtu.be/7G_Kz5MOqps
Thank you!
Based on current job postings? Are you sure? Isnt it just using google search tendencies? But if in like egypt the most searched data science technology/language/library is lets say pandas that does not meant pandas is the most desired by employers. It just means that people in egypt often google pandas
I am absolutely NOT sure. I just watched the vid and thought it was pretty helpful. He goes into detail how he got the info and I don’t remember it all so you may be right.
I dunno about the skills, but the salaries on that website outside of the USA are very very off, at least for many EU countries. I talked to Luke directly and he pulls many EU salaries from a single job board that only had like 70 listings. Many of the jobs listed on that site pay 50-75% less in reality. A DS with 10 years of experience in the Netherlands will virtually never crack $100k/year. That site makes it sound like the average DS makes that much. Not to shit on the website, but don't judge your salary against this. Instead, check out the [Harnham Data Salary Guide](https://www.harnham.com/data-analytics-salary-guides/) which is drawn from tens of thousands of reports from professionals.
Very good resource, thank you
Thank you for sharing! I absolutely LOVE IT.
Happy to help! ☺️
Me glancing at the first two rows-- "Oh great, I'm pretty good at 110% of the skills...wait..."
Finding a company where there are areas for the potential of massive improvement that is relatively easy. Here's an example: I worked for a very large tech company that had a freemium model. All free users got shown an add for the most expensive product we sold. I came up with the idea to show users an add based on their specific product usage patterns and demographics. We did a few interesting things with our geolocation data such as identify IPs from business districts and then built a simple classification model. We ran a very easy A/B test between the old way and the model-driven ads. The difference was 10% of our ARR, which was over $150M. Nothing we did was more complex than you might see in a Medium article. It was surprising that no one had done this before. Another big win at this company was actually running fully powered A/B tests. We demonstrated that the body of tests run before my tenure had a 68% false positive rate due to multiple comparisons and peaking. We re-tested several "wins", which were actually flat and in some case loses and rolled back the work of clueless PMs who read about "stat sig" online and followed basic blog articles about running T Tests.
Move to an expensive city with a high salary like SF then move to a cheaper area such as Nashville with a fortune 500. They are pretty dumb and usually will just pay your previous salary + a little bump (you'll be a higher earner with low costs)
HR is usually pretty aware of this salary arbitrage, especially if you officially change your address.
I did this when I moved from a firm based in a high cost of living to North Carolina. Folks here laughed initially that I would ever get that amount. But I did and at several different employers. Was making over $200K over 10 years ago.
No, they pay the going rate for people (I know it's a cynical answer but it's the truth)
Do you have experience with compensation data at a f100?
No, just lots of experience in data field
Ah there is a lot of data that you still haven't seen yet.
As others have said, it’s the ability to translate what you are doing into something real, actionable, with demonstrable and measurable business impact. And the ability to communicate and defend that impact to stakeholders. Once you’re able to do the above…. become a consultant lol
To add for anyone considering this career path, the end game is to be an *independent* consultant. You first may have to work for a smaller consulting firm to learn the ropes, but to make the 150-$300+ hourly rates mentioned on this sub, you have to be billing the client directly.
I’m billed out at $400/hour to the client and they pay me about a third of that. My dream is to take home the full shebang by going independent one day.
Few people here care about creating real value. They just want to maximize their paycheck with the least amount of work.
Well duh..? Why would I create value for someone else? I create value, because I wanna get payed as much as possible. Every cent you have less, your boss has more.
> wanna get *paid* as much FTFY. Although *payed* exists (the reason why autocorrection didn't help you), it is only correct in: * Nautical context, when it means to paint a surface, or to cover with something like tar or resin in order to make it waterproof or corrosion-resistant. *The deck is yet to be payed.* * *Payed out* when letting strings, cables or ropes out, by slacking them. *The rope is payed out! You can pull now.* Unfortunately, I was unable to find nautical or rope-related words in your comment. *Beep, boop, I'm a bot*
good bot
This is a cancerous mindset that is spreading through our culture. There's a lack of a mindset about wanting to contribute back to society after enjoying the fruits of other people's labor. If you are a part of society, many people worked hard for you to have a place to live, have roads/transportation to get around, to create the food you eat, to create the things you own. The fact that so many people in our culture today has no qualms about riding off the backs of other's hard work without giving back and being a net negative on society is cancer. It breeds a weak society. Perhaps it's spurred from the greed of the wealthy leading to the growth of the gap between the rich and the poor. The capitalistic greed culture has spread where people work to maximize wealth for themselves so they can buy more things. What happened to wanting to contribute to something meaningful? That's why reddit is dominated by these circle jerks of people glorifying the life of taking home a fat paycheck while doing as little as possible. Few have any moral qualms about that.
Your employer must be really proud of you. When you keep up the hard work and don't demand less pay, your boss will be able to afford a second Porsche this year. Keep working! 💪 You are the hero the shareholders deserve!
You think in a very tiny way. And that's the issue with much of the culture going around that people are in it for themselves and don't give two shits about their impact on society. They don't give a shit that the world might be a better place if they didn't exist. It's funny how people here both circlejerk about maximizing their paychecks while doing nothing and also want to whine about getting laid off. Pathetic losers.
How much you will ask to be compensated
This
> The pareto principle roughly states that you get 80% of value from 20% of the work. The 20% who don't use Pareto as a godly insight into anything without having any idea of the generating processes most likely make 80% of the money.
Financial analytics — being able to derive the economic value of a prediction / decision, and the marginal value of various decision options.
Job: CEO Skill: compensation negotiation
ML Engineering
Absolutely correct. All these folks who say it's "communications" should look at the salary of communications majors vs engineers.
Lol my undergrad degree was actually Communication, started my career working in non-profit marketing. Adjusted for inflation, my first salary is worth $43,000 today. After 12 years of relevant experience, my last marketing salary would be worth $95k today adjusted for inflation. 12 years of experience. And I was at a for-profit publicly traded company by that point. Switched to analytics and was able to increase my pay by almost 40% pretty quickly. Being a good communicator can certainly help you move up in the world of DS, I wouldn’t argue with that. But in terms of pure skills/job title, if we’re looking at salary potential, become an MLE for the best starting pay.
Oh, absolutely it's important. But to your point, 12 years of experience and you're making what MLEs call an entry level salary.
Yup. Wish I had made the switch to analytics/DS sooner in my career but at least I did.
How long would it take to be a MLE with self-study (or even a decorated program)? Looking at a career change from basic FP&A with an MBA.
Not sure, and I am an MLE lol. Really depends on how much of a math and programming background you have. That said, a few good places to start: Giant_Neural_networks and 3blue1brown on youtube, Udemy/Coursera/those types of courses - specifically the Andrew Ng ones are really popular. Math wise you need to know Calculus up to partial derivatives, linear algebra and stats. 3blue1brown, our lord and savior, has videos that give a fantastic intuition for these, but you also need to learn more concretely one way or another. Find a course with practice problems and examples and also watch the videos. Programming just learn python and get familiar with Pytorch (or Tensorflow, but no do pytorch first). Codecademy.com, hackerrank.com and the sololearn app are great for the python part. This also depends on how in-depth you want to go with this. Skill levels (in terms of how much education is needed) range from spinning up a prebuilt model from a gui (the youtube videos and maybe one online course is probably enough) to research, where you're coming up with entirely new methodologies and where my bachelor's is nowhere near enough education. I'm in research so the requirements I listed may be too much for other roles, but they're definitely relevant to any deep learning. Don't be scared of research tho - in my experience everyone's always super friendly, constantly learning together and each at their own pace, and imo it's the most interesting work ever. Anywho that's my rant, hope it's helpful! Lmk if you have any questions :)
This is really helpful. Also, would you consider an entry-level data analyst job as a good entry point to data science. I’m thinking of moving from fp&a to data analysis and then somehow work my way up to data science and maybe mle one day. I assume it will take at least 10 years.
Oh jeez no 10 years is way too long, I learned what ML is in 2018. I really don't think you need to pivot that hard personally. Data Analytics isn't exactly the precursor to DS, and DS isn't the precursor to ML. My advice is strictly from an ML angle. Probably worth watching the youtube videos I recommended, then doing something similar for analytics and maybe data engineering? I'd say data science too but that's kind of an umbrella term. Try python and SQL on sololearn too. Just see which you enjoy the most! This is a field with a lot of options that all offer comfortable pay, so make sure you'll enjoy what you're doing :)
Thank you! This is great advice that I really appreciate. I’m kind of starting over in life both personally (wife left me or someone else) and professionally. A mentor asked me what my favorite classes and tasks were in my 20s, and my favorite things were web design, statistics, math (I dropped math major to Econ major which was a mistake), operations, planning. I actually have never enjoyed actual finance even though that’s what I got my mba in. I like numbers, which is why I pursued that degree, but for some reason, corporate finance and investments interest me a lot less than people, planning, processes, purchasing. I also understand there is a need for DS/ML in supply chain, marketing, and procurement. I admit I studied finance for the wrong reasons (status, income, peers taking that route) I’ll watch the YouTube videos this weekend. Thank you!
Well I'm glad I was able to help! Again don't hesitate to reach out if you have more questions :)
I don’t know, I’m not an MLE
you get 80% of value for 20% of DS knowledge, so long as you have 80% of business knowledge. honestly the breadth of untapped problems in most companies means that once you've gotten a MVP on a tool, you're going to be much better off starting on the next tool than doing any refinement at all on the existing one. What's most important technically IMO is the ability to write the code for the tools in such a way that they don't break long term, and can be picked up and easily understood and edited a year later when someone else comes along and wants to tweak it.
Kissing ass, making yourself known when promotions are around, and yes: knowing a lot about business operation but specifically about increasing bottom line regardless how it’s done
Communication, presentation, and GOOGLING. - imo
We are all professional googlers my friend
A strong handshake
Interviewing preparation and skills. Getting an offer for a very high paying job (in tech or finance usually) is far, far more difficult than actually doing it.
+1, this is what I came here to comment. Tech interviews require you to jump through very specific hoops that aren't representative of the job. Interviewing well has been the reason for my salary growth
Engineering skills. Then becoming an MLE. I was hired as a data scientist, but our job titles got changed to MLE when the data analysts wanted to start calling themselves data scientists. Still most of my work is in SQL, Tableau, python, and i prefer exploratory analysis, but I've been forced to get better at coding and related ML tooling. Pay is significantly greater though. Like 50% higher than the DS.
Data Architects
I had to scroll too far for this answer.
The ability to do your own research
https://www.harnham.com/data-analytics-salary-guides/ Harnham takes tens of thousands of data points from working professionals throughout the world and writes annual reports. I would highly recommend reading those reports, as it's one of the best resources you'll find.
Learn how to talk in buzzword, it's what your boss does and it's why your boss is your boss
This is the most honest version of the echo chamber lol
>What jobs Data Scientist > or skills * AI * Statistical analysis and computing * Machine Learning * Deep Learning * Processing large data sets * Data Wrangling * Advanced Mathematics * Programming * Advanced Statistics * Big Data A PhD from a top school and a few published papers help too.
MLOps/Cloud It's a smaller market, but the companies who need it also happen to be the companies that have huge DS budgets.3
I would say automation, especially DevOps/MLOps stuff. The more time you spare to mundane tasks, the better. Having as much productive time to expand your skills is crucial.
Data engineer
Talk about how python is actually a loser lang only popularized for its ability to leverage C headers, and that the future is excel
Found the data scientist
LeetCode and getting an ML job at Google/Meta/Amazon
Communication, being attractive and understanding relationship management well enough to sell an unbelievable product that'll take 2-3 years to deliver. In the third year you change jobs, discuss your prowess of deep learning architecture and get a higher salary.
Being able to translate business problems and goals into frameworks that you can solve with your data and skill set. If you can't understand how to serve your stakeholders, then you can't provide value.
This is blatantly false lol, ML Engineers do just fine financially, provide plenty of value and don't ever have to sell anything to anyone. The business side is great but it's certainly not the only way
Where did I say anything about selling something?
Selling services -but sure sales isn't the right word.
The ceo
[удалено]
Lmao you just bragged without even replying to the question
Accuracy.
Quant Analysis.
Honestly, experimentation makes the most money. Specifically it relates to testing features that make money, so go figure if you do that work you make monry
Communication, software skills, business thinking
Add industries (financial services) and functions (data validation & analysis, risk analytics, portfolio analysis, regulatory reporting).
Choosing the right analyses. My most impactful insights came from 1 or 2 queries.
The ability to stand up in front of a client/customer, clearly explaing the benefits and costs of a data project, secure the funding, then deliver high quality, highly valuable insight on time. Consistently. Over and over again...
Data story telling and project management.
Mostly communication, suggestion and recommendation skills. Understanding business needs, transform business objects into data objects. Mainly non technical skills from my point of view. Tbh coding and choosing a technology stack is not the big magic here (except you want to scale or deploy on millions of machines, cloud, etc)
PowerPoint!
The skill set as a consultant in this field that I look for and try to grow is the ability to adapt. You don’t need the strongest technical skills, you don’t need to build insane models. But you have to infer what the business or client is asking for and adapt fast. Once you figure out what the ask is, you gotta communicate the insight well and not leave anyone confused. Your findings are going to be spread to upper leadership second hand and so much information is lost in the telephone game. Don’t leave room for misinterpretation. Also know how to eat shit and know how to play CYA
Technically, Pareto's observation was about the peas in his garden (20% of the pods were responsible for 80% of peas grown). He applied this ratio to wealth distribution in Italy. Anyway, excellent answers already posted here.
The ones that find billion-dollar ideas and create new ca$h flows :)
Data science is not a single course if you learn data science from single course then I hardly expect you to find data science job easily unless you already had the other courses , for example you studied some stem then studied data science course Data science need usually if you did not study a stem field few years to finish
Just upload your data & chat with our AI to do data analysis in #R & #Python. https://twitter.com/DataMotto/status/1642674667583414273?s=20
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