Design, build, and operate production ML systems for rewards optimization, recommendation/ranking, uplift and causal measurement, and experimentation. Own feature pipelines, training, deployment (batch/APIs/services), monitoring, retraining, and model lifecycle. Mentor teammates and establish ML engineering standards while delivering data products from prototype to production.

Design, build, and operate high-frequency, event-driven data pipelines into Snowflake; implement reverse ETL; model data with dbt; deploy cloud infra as code; monitor and improve production reliability, latency, quality, and cost; mentor teammates and establish data engineering standards.

Lead data science projects end-to-end: define high-impact problems, build and productionize models (recommendations, uplift, forecasting), own experimentation and measurement, operate reliable pipelines, and mentor the team to drive business metric improvements.