Shira Mitchell is a statistician who received her B.A. and Ph.D. from Harvard University. For her dissertation, she worked with the Human Rights Data Analysis Group (HRDAG) to develop Bayesian hierarchical models for estimating numbers of casualties during the armed conflict in Colombia. She then did her postdoc at Columbia with Andrew Gelman. She worked at Mathematica Policy Research on small area estimation and causal inference for federal agencies (mostly Medicare and Medicaid). She then worked at the NYC Mayor’s Office of Data Analytics (MODA), working with city agencies to deploy and critique data-driven policy. Today, she uses statistics to advise Democrats.
Please describe your specific field of work and what it is that sparked your interest. Are there any other careers you have considered and what events have led to where you are today?
I am an applied statistician who has worked on projects in public health, human rights, policy, and politics. I studied (theoretical) math in college and loved it, but did not want to stay in theory. I also did not want to go into finance (which many of my peers were doing, though I graduated during the Great Recession…). I wanted to find where math was being applied to make the world better. By wonderful luck, I met a statistics Ph.D. student who was working on public health projects in Tanzania. Probability was my favorite math class, so statistics sounded wonderful. I ended up following in her footsteps to get my Ph.D., work in Tanzania, and then to work with her on US health care policy. To this day, she is my hero and friend.
How would you describe your interaction with math during high school?
Math was my favorite class in high school, and I was on the math team. Also, my parents are both mathematicians! But I wasn’t particularly precocious, I did not skip grades or do advanced competitions. Instead, I focused on solidly understanding “the basics” and explaining concepts to folks who wanted tutoring. I learned that my superpower wasn’t speed, but crafting careful, clear explanations. My favorite thing was and still is resolving confusion (both my own and for others).
As a woman, have you faced any hardships throughout your career? If so, how did you overcome them?
Yes, I’ve faced both sexism and misogyny. It helped enormously not to face them alone, I’ve had so much solidarity and support (from all genders). Being one of few (or the only) woman in a room can be uncomfortable, or even terrifying. For the most part, I’ve learned how to not settle for anything less than people who help me feel safe, welcome, and listened-to.
Where do you see yourself 10 years from today?
Right now I’m working in electoral politics (did you hear that there is an important election in 2020?). I also loved working in government, so I can definitely see myself returning to public service. Today most statistical expertise is concentrated in academia and industry, but governments make a lot of decisions that impact our lives. Statistics is a powerful tool that all too often is used to make rich people even more money. But it can also be a tool to help dismantle social hierarchies and empower communities. I want to be of service to those goals and the people working towards them.
What advice would you offer to youth today looking into pursuing mathematics? As one myself, I am grappling with how to cause tangible impacts in other people’s lives. From my limited background, it seems as if impacts associated with many math-related careers seem rather abstract than tangible. I wish to hear your thoughts as a human rights data analyst.
To be honest, I’m still grappling with that too. I’ve moved closer and closer to seeing those tangible impacts, going from theoretical math, to statistics academia, to policy research, to implementation in government. Every one of those spaces does important work. So it’s a matter of figuring out where you are having fun. I love the excitement (and let’s face it, chaos) of being close to the tangible impacts. But even though I left paper-writing academia, I still love writing things up when I figure stuff out. Wherever you end up in that pipeline, I would encourage you to think: how is your work being used? by whom? is it doing harm? is it helping?