Building a Satisfying Career in Data Science

What is the best advice for a satisfying career in data science or machine learning? originally appeared on Quora - the knowledge sharing network where compelling questions are answered by people with unique insights.

Answer by Monica Rogati, Data Science advisor, formerly the VP of Data at Jawbone and LinkedIn, on Quora.

The definition of 'satisfying' varies greatly from person to person, so I would first encourage you to think about what it means to you.

Are you excited by constantly learning and stretching your boundaries? You'd like to have impact, but what does that mean? Is it a social mission, drug discovery, making a business run more efficiently? How important is quality of life? Are you excited by using or inventing new algorithms, or is it more important to see outsized results given your time investment, even if that means you're using a boring heuristic or an algorithm that's out of fashion? Do you like managing people and spending 50% of your time building and retaining the team? Do you thrive on external recognition or does the thought of giving a talk makes you ill? Does the thought of writing production code that you're responsible for debugging in the middle of the night make you feel alive and important, or hassled? How important is compensation, and how important is its predictability and what's the cash/equity ratio? Are you at peace with spending 100K in opportunity cost/lost salary to work at a nonprofit that matches your mission or a startup where you'll learn more than at a top university, or will you become resentful? How important is culture fit to you?

Be honest with yourself and try not to think about how you should feel. There's no shame in deciding you'll only consider a less than 30 min commute or that you don't want to be the first woman/ POC on a team; your mental energy and time are a limited resource and whether you think they "should" or not, these factors matter in evaluating how satisfying a particular job is, and ultimately your career.

Once you've thought about those questions, it's important to find an environment that matches your priorities, and a company and team that shares your values. Do a thorough job search, otherwise you won't make an informed decision. Data science and machine learning are in demand, so you're in a position where you will likely have many great offers. When you have a few of these offers on the table, it's time for the "puppy dog test".

The Puppy Dog Test for deciding your future:

OK, your phone is buzzing with offers and your future looks bright. Before making a decision about which company to join, imagine yourself at a cocktail party/game night, talking to your peers or other people whose opinion matters to you. If you can, don't imagine it, but actually do it -- invite a few people over.

Tell each of them about your top 2-3 choices in 2 sentences: 1) what the company does (if not a household name) and 2) what your role would be and what you'd do there.

"Initech builds wheelchairs you can control with your brain. I'd be their first data scientist and I'll work on route prediction for faster, smoother control."

Now, the standard advice is to ask for feedback and see what people think and what questions they ask. That's good advice. However, what's even more important is to pay attention how you feel when talking about it.

Do you feel like a puppy?

Is your brain wagging its imaginary tail? Are you so excited you could barely contain yourself, even if you're not usually "the type"? That's a good sign. Take that offer. Because it's very likely that's the most excited you're ever going to feel about the job. You might love it more as you learn more about it and as you grow, but that pure infatuation and enthusiasm unfettered by the day-to-day reality is probably at its peak.

This is even more important if you're joining as an executive, or as somebody who has a significant role in recruiting a team or talking to investors. That authentic enthusiasm is infectious, it's palpable, and people will join you so they can feel that way, too.

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