[jakek]

For the data science (DS) program we’re very open to strong quantitative fundamentals without ML experience, as many of the DS roles are focus on analysis. For the AI program, since the focus will be entirely on getting you up to cutting edge of what’s happening in ML, it will be important to already have had experience with the fundamentals of ML. Luckily, for someone with your background, it’s completely possible to build some impressive side projects in a couple of months than can demonstrate your ability to learn ML at a rapid pace. We’re far more interested in how quickly you can learn than your current level of knowledge, so doing a side project and mentioning your starting point prior to the projects will give us strong signal that if we accept you into the fellowship you’ll be able to pick up the necessary techniques.

As an aside: it’s astounding how quickly Fellows with strong quantitative backgrounds can learn when surrounded by other really great people. We’ve had numerous mathematicians and theoretical physicists at Insight who barely touched any data in their PhDs, go on to build sophisticated machine learning data products at Insight and get hired at top tier data science and machine learning teams.

Regarding the location: the initial programs will focus on roles in the SF Bay Area and New York, so we’d like to attract Fellows who are interested in living in either of those respective cities. That said, our network has grown well beyond those cities so I encourage you to apply regardless of geographic preference and just let us know in the application where you hope to end up after the program.