Data Science Intern

HireSetu's note

Both degree tracks, same work

Doctoral and master's candidates are both eligible, in statistics, econometrics, economics, operations research, computer science, maths and neighbouring fields. The remaining-semester rule matters more than it looks: the internship has to fit inside your degree, so your graduation date decides whether you can take it at all. Statistics and validation are named as skills, and the day-to-day asks you to interpret results and check your own approach. Most student projects stop at a trained model and a score. The validation half is where they are thinnest, and it is half of this job.

The week is exploration and rework

Expect a long stretch of digging through data for questions worth asking, then arguing about what those numbers can and cannot support: which users are covered, which time window, what is missing. The posting names applicability and limitations of data outright, so writing that section well is part of the job, not a disclaimer tacked on at the end. Peer review is built into the loop, so someone reads your method and pushes back. Then you monitor and iterate.

Clearing up the analyst comparison

A data analyst answers the question that lands on their desk, on a schedule. This internship asks you to find new questions inside a problem area and tie your findings to product and customer context, which is why stakeholders are described as peers. The deliverable is an insight a product team can act on, and it has to survive their scrutiny. The work stays open-ended in a way ticket queues are not.

Drafted with AI from Microsoft's job posting and checked by Prashanth Jakkula before publishing. See our Editorial Policy.