Data Scientists: Five things to ask yourself about your career this holiday season

It’s the holiday season coming up on us again — seems to come around quicker every year, doesn’t it? I’m not complaining of course. I always need the break and it’s a nice period to relax, unwind, open some gifts, play some computer games, watch some TV, and of course eat an unholy amount of festive food.

But for me it’s also a time to reflect and take stock. The field of analytics and data science is moving like a freight train — making unstoppable strides forward. New technology and methodology is popping up at a rate most of us struggle to keep up with. Conferences on tools and theory abound. Companies are hiring data scientists like crazy — they can’t get enough of us even though some have no idea what to do with us.

This may not last forever, so it’s up to us to make the most of the data science bull market while we can, and the holiday period is a good time to ask yourself if you are in a good spot or whether you can do better. To help you, here are five questions that I ask myself every holiday season.

1. Am I having impact?

Data scientists should be able to see how their work is helping others. There are many ways a data scientist can have impact — they could generate killer insights that help with big decisions, they could make data and measures more available to people around them, they could teach new ways of doing things.

Is someone or something benefiting from your work? Are you happy that the effort you put in has meaning and effect? If you feel you are churning out stuff but you have no idea why or for what purpose, maybe it’s time to rethink things?

2. Am I understood and respected?

The role of a data scientist — what they are trained to do and what skills they bring to the table — should be well understood by someone in authority where you work. If it’s not, you may find that work is coming your way that has nothing to do with data science, or that you have no interesting work to do at all.

Do you feel that you were hired based on a proper understanding of your skill set? Do those that you work for know how to get the best out of you? Is sufficient regard and kudos attributed to you and the skills that you bring with you? If not, it’s at least worth a conversation don’t you think?

3. Am I constantly learning?

At the rate things are moving in this space, if you are not constantly learning and keeping on top of developments, you’ll be much less hireable in a couple of years.

Does your workplace facilitate you learning new skills in the way they assign work to you? Do they support you taking time out to learn relevant new stuff? Are you encouraged (or at least permitted) to interact with the community and participate in events that help you learn and keep in touch with developments? If not, aren’t you just heading into another year of churning out the same old, same old just to make a dollar or two?

4. Do I have the technology I need?

Keeping up with technology infrastructure in data science is just as important as keeping up with content. v1.0 of your current tool might be v8.0 within a couple of years, or it might even be something else entirely.

Are you happy that you can get access to the toolchain you need as it develops? Is your environment supportive and flexible in adapting its technological infrastructure to support your needs and to keep you current? If not, you should make your voice heard!

5. Am I building in confidence?

It’s not all about technical growth, a data scientist needs to build in confidence through their career. They need to become better communicators and more authoritative in their field — in the long run inspiring and mentoring the up and coming data scientists that are never very far behind.

Do you feel that you are in an environment that encourages you to build confidence, presentation and communications skills? Are you put in situations that facilitate the development of these skills? Do you think others see that your presence is getting bigger every year? If not, take action — don’t get labelled as the reliable back-office data scientist.

These are some of the things I think about — and give advice to others on — every year. I hope they help you take stock. Enjoy the holidays!

I lead McKinsey's People Analytics and Measurement function. Originally I was a Pure Mathematician, then I became a Psychometrician. I am passionate about applying the rigor of both those disciplines to complex people questions. I'm also a coding geek and a massive fan of Japanese RPGs.

All opinions expressed are my own and not to be associated with my employer or any other organization I am connected with.