About the RoleHinge Health helps people move without pain through digital musculoskeletal (MSK) care. That care only works when members keep doing their exercise therapy, and the right message at the right moment is a large part of what keeps them going.
As a Staff ML Scientist on the Proactive Communications & Notifications team at Hinge Health, you'll own the machine learning that decides what message each member receives, when, and through which channel. At our scale, small gains in relevance and timing compound into large gains in engagement and clinical outcomes.
You'll be the technical leader for ML on the team: setting direction for send-time optimization, propensity modeling, and the experimentation rigor behind every nudge we ship. You'll write code your senior engineers respect, mentor a small ML team, and partner closely with product, data science, and our growth and marketing teams.
Our ideal candidate has shipped recommendation or sequential-decisioning systems that changed how real users behave, runs experiments with rigor, and writes code their engineers respect. They optimize for what moves for members, not model sophistication for its own sake.
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
