We are seeking an early-career research scientist.
You will take open, loosely defined problems from first question to a working, validated, defensible result. This is hands-on applied research: you will own the problem end to end, build the solution yourself, and be accountable for whether the evidence actually holds.
What you will do
- Take ownership of applied machine-learning research problems end to end: framing the question, assembling and cleaning the data, choosing and implementing the method, evaluating it honestly, and reaching a defensible conclusion.
- Build and validate models on messy, real-world data, with rigorous attention to data quality, data leakage, regime change, and genuine out-of-sample performance.
- Implement methods from first principles where needed, in Python and, where appropriate, a compiled or systems language.
- Design evaluation harnesses and benchmarks so that your own results, and the team's, are reproducible and falsifiable rather than merely asserted.
- Communicate your reasoning and results clearly to both technical and non-technical colleagues, and independently reproduce and pressure-test teammates' results.
- Work collaboratively in a small, high-density team, and raise the way the team works, not only your own output.