Senior Data Scientist

Join us to build the decision engine for better mental health outcomes.

As a Senior Data Scientist, you will sit in the heart of a cross-functional product team and help turn messy, real-world signals into clear decisions. You will make sure we are capturing the right data, designing experiments that tell us what is actually driving outcomes, and translating findings into recommendations that teams can act on quickly.

When the insight is stable and valuable, you will help operationalize it through predictive models that improve provider and patient experiences.

If you like ambiguity, enjoy hard problems, and want your work to matter in the world, you will fit right in.

- Be the analytics partner inside the pod. Work closely with Product, Engineering, Design, Ops, and Clinical stakeholders to define the questions, the metrics, the guardrails, and the decision rules.

- Run rigorous experiments. Design and analyze A/B tests and quasi-experiments with clear hypotheses, power considerations, and pre-defined success criteria.

- Connect behavior to strategy. Use funnel, cohort, segmentation, and lifecycle analysis to understand how people and providers experience Headway, and where product changes will have the biggest impact.

- Use causal inference when experiments are not possible. Apply approaches like diff-in-diff, matching, and regression-based designs with principled uncertainty quantification.

- Build models when they should exist. Develop predictive models that operationalize vetted insights (feature development, validation, backtesting, calibration), with clear launch criteria and monitoring plans.

- Create decision-ready work. Produce analysis and narratives that are crisp, honest about uncertainty, and drive action.

- Help raise the bar. Contribute to shared standards for metrics, experimentation, and analytical quality, so teams can move faster with confidence.

- 6+ years using data to drive product or business decisions in product, growth, engineering, or operations environments.

- Strong SQL and strong proficiency in Python or R for analysis and modeling.

- Demonstrated depth in experimentation and causal inference under real-world constraints. Bayesian experimentation experience strongly preferred.

- Practical modeling skill: feature engineering, model comparison, cross-validation or backtesting, calibration, and post-launch monitoring.

- Strong product sense and opinions, including a track record of connecting analytics recommendations to measurable outcomes.

- Clear communication: you can explain complex work to non-technical audiences without losing the truth.

- A self-starter mindset: you prioritize well, follow through, and do not need heavy oversight.

- Motivation for our mission: improving access and affordability in mental healthcare.