Regarding the data-driven policy path, my sense is that unfortunately, most policy work in the U.S. today is not that data-driven, though there’s no doubt that that’s in part attributable to human capital constraints. Two exceptions do come to mind, though:
Macroeconomic stabilization policy (which is one of Open Philanthropy’s priority areas) definitely fits the bill. Much of the work on this in the U.S. occurs in the research and statistics and forecasting groups of various branches of the Federal Reserve System (especially New York, the Board of Governors in D.C., Boston, Chicago, and San Francisco). These groups employ mathematical tools like DSGE and HANK models to predict the effects of various (mainly but not exclusively monetary) policy regimes on the macroeconomy. Staff economists working on this modeling regularly produce research that makes it onto the desks of members of the Federal Open Markets Committee and even gets cited in Committee meetings (where U.S. monetary policy is determined). To succeed on this path in the long-term you would need to get a PhD in economics, which probably has many of the same downsides as a PhD in computer science/AI, but the path might have other advantages, depending on your personal interests, skills, values, motivations, etc. One thing I would note is that it is probably easier to get into econ PhD programs with a math-CS bachelor’s than you would think (though still very competitive, etc.). The top U.S. economics programs expect an extensive background in pure math (real analysis, abstract algebra, etc.), which is more common among people who studied math in undergrad than among people who studied economics alone. A good friend of mine actually just started her PhD in economics at MIT after getting her bachelor’s in math and computer science and doing two years of research at the Fed. This is not a particularly unusual path. If you’re interested and have any questions about it, feel free to dm me.
At least until the gutting of the CDC under our current presidential administration, it employed research teams full of specialists in the epidemiology of infectious disease who make use of fairly sophisticated mathematical models in their work. I would consider this work to be highly quantitative/data-driven, and it’s obviously pertinent to the mitigation of biorisks. To do it long-term, you would need a PhD in epidemiology (ideally) or a related field (biostatistics, computational biology, health data science, public health, etc.). These programs are also definitely easier to get into with your background than you would expect. They need people with strong technical skills, and no one leaves undergrad with a bachelor’s in epidemiology. You would probably have to get some relevant domain experience before applying to an epi PhD program, though, likely either by working on the research staff at someplace like the Harvard Center for Communicable Disease Dynamics or by getting an MS in epidemiology first (you would have no trouble gaining admission to one of those programs with your background). One big advantage of epidemiology relative to macroeconomics and AI is that (my sense is) it’s a much less competitive field (or at least it certainly was pre-pandemic), which probably has lots of benefits in terms of odds of success, risk of burnout, etc. Once again, feel free to dm me if this sounds interesting to you and you have any questions; I know people who have gone this route, as well.
Regarding the data-driven policy path, my sense is that unfortunately, most policy work in the U.S. today is not that data-driven, though there’s no doubt that that’s in part attributable to human capital constraints. Two exceptions do come to mind, though:
Macroeconomic stabilization policy (which is one of Open Philanthropy’s priority areas) definitely fits the bill. Much of the work on this in the U.S. occurs in the research and statistics and forecasting groups of various branches of the Federal Reserve System (especially New York, the Board of Governors in D.C., Boston, Chicago, and San Francisco). These groups employ mathematical tools like DSGE and HANK models to predict the effects of various (mainly but not exclusively monetary) policy regimes on the macroeconomy. Staff economists working on this modeling regularly produce research that makes it onto the desks of members of the Federal Open Markets Committee and even gets cited in Committee meetings (where U.S. monetary policy is determined). To succeed on this path in the long-term you would need to get a PhD in economics, which probably has many of the same downsides as a PhD in computer science/AI, but the path might have other advantages, depending on your personal interests, skills, values, motivations, etc. One thing I would note is that it is probably easier to get into econ PhD programs with a math-CS bachelor’s than you would think (though still very competitive, etc.). The top U.S. economics programs expect an extensive background in pure math (real analysis, abstract algebra, etc.), which is more common among people who studied math in undergrad than among people who studied economics alone. A good friend of mine actually just started her PhD in economics at MIT after getting her bachelor’s in math and computer science and doing two years of research at the Fed. This is not a particularly unusual path. If you’re interested and have any questions about it, feel free to dm me.
At least until the gutting of the CDC under our current presidential administration, it employed research teams full of specialists in the epidemiology of infectious disease who make use of fairly sophisticated mathematical models in their work. I would consider this work to be highly quantitative/data-driven, and it’s obviously pertinent to the mitigation of biorisks. To do it long-term, you would need a PhD in epidemiology (ideally) or a related field (biostatistics, computational biology, health data science, public health, etc.). These programs are also definitely easier to get into with your background than you would expect. They need people with strong technical skills, and no one leaves undergrad with a bachelor’s in epidemiology. You would probably have to get some relevant domain experience before applying to an epi PhD program, though, likely either by working on the research staff at someplace like the Harvard Center for Communicable Disease Dynamics or by getting an MS in epidemiology first (you would have no trouble gaining admission to one of those programs with your background). One big advantage of epidemiology relative to macroeconomics and AI is that (my sense is) it’s a much less competitive field (or at least it certainly was pre-pandemic), which probably has lots of benefits in terms of odds of success, risk of burnout, etc. Once again, feel free to dm me if this sounds interesting to you and you have any questions; I know people who have gone this route, as well.