Who thrives here
- You've shipped a model to production and watched what it did there. You have a specific recent example of something you built, owned, and put in front of real decisions, and you care about what happened after it shipped. Notebook-to-nowhere work is not what this is.
- You're comfortable with ambiguity at the problem level. The interesting problems here don't arrive as labeled datasets with a defined target. You hear something from a customer, decide what's worth predicting, find or build the signal, and ship it. If you want the problem fully specified before you start, this role will frustrate you.
- You find the domain genuinely interesting. The compliance constraints, the quality and logistics tension, and the fact that your model's output moves medication to real patients should read as compelling design constraints, not obstacles.
- You're honest about what you don't know. We debate hard, change our minds, and challenge each other, always assuming everyone in the room is trying to get it right. A model that looks good for the wrong reason is something you want to catch, not defend.
- You don't think customer contact is a tax on your time. The ops people on those calls will tell you which errors actually cost something. That is signal you can't get from the data alone.
This role is probably not right for you if you prefer to go deep on one research problem and be left alone, or if you measure success by offline metrics rather than shipped impact. The surface area is wide: detection models, feature infrastructure, experimentation, and agent evaluation. If switching between modeling and infrastructure drains you, this will too.
What we're looking for
You have 3-9 years of applied data science or ML experience. You've put models into production, with the evaluation and monitoring that keeps them honest, not just trained them offline. You're strong in Python and the modern data and ML stack. You're comfortable owning data infrastructure: pipelines, feature engineering, and the messy work of making real-world data usable. You have real experimentation rigor and know the difference between a model that scores well and a model that works, and you can design the test that tells them apart.
You understand data modeling and how your work connects to the systems around it. You're based in San Francisco or Chicago and excited about being in the office.