You're a statistician with multiple career options. How do you decide which path to take?

There's always more than one way to reach a career goal, and many of those options won't have the job titles you might expect, so be very broad in your search. Also, try different things, because you never know what you'll like until you get there. I never meant to have my career focus on data, and I was terrified when I was first asked to teach a statistics class, but now I love it. Try new things.

Considering long-term career goals is pivotal for charting a fulfilling path in machine learning. Identifying preferred project types, desired impacts, and skill development areas shapes career trajectories. Those aspiring to pioneer research may gravitate towards academia or research institutions, fostering creativity and autonomy. Conversely, individuals eager to tackle real-world challenges might opt for consulting or industry roles, promoting collaboration and tangible impact. By aligning career choices with personal aspirations, you can cultivate rewarding journeys that not only enhance your expertise but also leave a lasting imprint on the field of machine learning.

It's too easy to chase shiny, high-prestige jobs that end up not being at all what you thought once you see them closely. (That's why I'm not a lawyer like my father; I had summer jobs in his office and knew I wouldn't be happy doing that.) Flexibility is a wonderful thing, too, and that's a great thing about working in data; you can collaborate with people from all sorts of different fields. As John Tukey said, "the best thing about being a statistician is that you get to play in everyone's backyard."

The challenge here is that self-knowledge can be elusive. That is, there is a strong temptation to claim values that are popular or prestigious. It's worth taking the time to look carefully at your own experiences and see (a) what you actually chose to spend your time on; and (b) when you actually felt most involved or fulfilled. Just make sure that one of your values is self-respect, so that you know when other people are showing you that same respect and when they aren't.

LinkedIn is a wonderful way to see what the opportunities are. I also recommend spending some time as a volunteer on data projects in different fields – any nonprofit would be thrilled to have your expertise for even a few days – so you can get the hands-on experience. Also, salary is important, but quality of life is even more important (and so is cost of living). Living and working outside of a major metropolis can have a better ROI both financially and personally.

Even if you work for the same organization and have the same job title for many years, your responsibilities and interests can evolve considerably. I have functionally had 3 completely different careers at the same place (and that's without going into management or administration). Allow yourself that flexibility.