- 5+ years building ML models that shipped and were used by someone other than you — in industry, not only in research or coursework.
- Strong Python and genuinely strong SQL. You should be comfortable being handed a warehouse and finding your own way around it.
- Solid grounding in classical ML – tree-based models, regression, classification, clustering — and the judgement to know when the simpler model is the right answer.
- Real rigour on experiment design and evaluation. You know why a metric moved, and when it didn't move for the reason everyone assumes.
- Comfort on a cloud data platform – we work in GCP (BigQuery) and Databricks, but the principles transfer.
- A quantitative degree, or equivalent practical depth.
- Working English, written and spoken.
Plus real depth in at least one of
- Causal inference and uplift modelling – separating who converts because of an intervention from who would have converted anyway.
- Contextual bandits, sequential decision-making, or off-policy evaluation.
- Ranking, recommendation or information retrieval.
- LLM evaluation, or applied work with the GenAI stack.
Also good: you use agentic coding tools daily and have a view on where they help and where they quietly don't. We build agents for a living, and people who use them tend to have better instincts about them.
The pay range actually offered will take into account a variety of potential factors considered in compensation, including but not limited to skills, qualifications, geographic location, accomplishments, experience, credentials, internal equity and business needs, and may vary from the range listed above.
