Staff Machine Learning Scientist
Own ML systems for send-time optimization, propensity modeling, and nudge decisions at consumer scale. Set experimentation standards and mentor a small ML team.
What You'll Accomplish
- Send-time and channel optimization: Design and ship the next system for deciding what nudge to send a member, when, and through which channel, beyond our current contextual-bandit approach.
- Propensity modeling: Build and deploy models that decide whether nudging a given member is worth it, balancing engagement against fatigue and unsubscribes.
- Experimentation rigor: Set the bar for how the team runs experiments: multi-arm tests, sequential testing, CUPED, and guarding against peeking, so our nudge decisions are causally sound.
- Production ownership: Own at least one model in production end-to-end.
- Leadership: Mentor the team's ML scientists, guide technical direction, and partner across product, engineering, data science, and the growth and marketing teams.
Required Qualifications
- Bachelor's degree or higher in Computer Science, Statistics, Operations Research, Machine Learning, or a related quantitative field
- 7+ years building and deploying ML systems in production at consumer scale
- At least one recommendation, ranking, or sequential-decisioning system shipped end-to-end (modeling, evaluation, deployment, monitoring, iteration)
- Fluency in experimentation and A/B testing: multi-arm tests, sequential testing, CUPED, and the common failure modes of online experiments
- Proficiency in Python and SQL; able to read a colleague's PR and improve it
- Deep understanding of machine learning and applied statistics
Preferred Qualifications
- Contextual bandits or reinforcement learning operated in production
- Multi-objective optimization (engagement vs. adherence vs. retention vs. cost)
- Causal inference beyond A/B testing: difference-in-differences, synthetic controls, instrumental variables
- Cold-start and low-data-regime modeling (healthcare gets thin on per-member data fast)
- Experience hiring and growing a small ML team
- Healthcare, fintech, or other regulated-data experience; familiarity with HIPAA and BAA constraints
- Familiarity with our adjacent stack: Statsig, Databricks, feature stores, Airflow/dbt
- Familiarity with TypeScript
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