Staff Data Scientist - Product Analytics

Headway’s mission is a big one – to build a new mental health care system everyone can access. We’ve built technology that helps people find great therapists with the first software-enabled national network of providers accepting insurance. 1 in 4 people in the US have a treatable mental health condition, but the majority of providers don’t accept insurance, making therapy too expensive for most people. Headway is building a new mental healthcare system that everyone can access by making it easy for therapists to accept insurance and scale their practice. Headway was founded in 2019. Since then, we’ve grown into a diverse, national network of over 60,000 mental healthcare providers across all 50 states who run their practice on our software and have served over 1 million patients. We’re a Series D company with over $325m in funding from a16z (Andreessen Horowitz), Accel, GV (formerly Google Ventures), Spark Capital, Thrive Capital, Forerunner Ventures and Health Care Service Corporation. We want your time here to be the most meaningful experience of your career. Join us, and help change mental healthcare for the better. About the role Join us to build the decision engine for better mental health outcomes. As a Staff Data Scientist, Product Analytics , you will be a senior technical and strategic leader for how Headway measures, learns, and improves. You will own the hardest and most ambiguous product questions, shape how we define success, and drive decisions that cut across multiple teams and product surfaces. You will not only run the analyses, you will influence what we build, how we evaluate it, and how we scale what works. This role is for someone who can zoom in to debug a metric definition and zoom out to design a measurement strategy for a major product area, then bring the organization along with them. What you will do Everything in the Senior role, plus: Own a product measurement strategy. Define north star and input metrics for a major product area (or multiple surfaces), align stakeholders on definitions, and build conviction in what “good” looks like. Lead multi-team initiatives. Drive cross-functional work where the right answer is not obvious and the path is not linear, and ensure we land measurable outcomes. Set the experimentation standard. Establish frameworks for test design, guardrails, decision rules, and interpretation that improve both speed and rigor across teams. Develop evaluation systems for complex products. For areas like matching, ranking, or applied AI experiences, build measurement approaches that combine offline evaluation, online experiments, and human-quality signals when needed. Build scalable analytical assets. Invest in durable measurement systems: semantic definitions, self-serve dashboards, monitoring and alerts, and repeatable templates that reduce ad hoc work. Mentor and uplevel. Coach other data scientists and analytics partners, provide strong technical review, and model what great looks like. Be a thought leader. Proactively identify opportunities, shape roadmaps with an evidence-based POV, and create clarity where teams feel stuck. What will make you successful 10+ years using data to drive product or business decisions in product, growth, engineering, or operations environments (or equivalent depth of experience and scope). Expert fluency in SQL and strong proficiency in Python or R , including building reliable, reusable analysis workflows. Deep expertise in Bayesian experimentation, causal inference, and measurement design , including common failure modes and how to prevent them. Strong applied modeling judgment: you know when a model is the right tool, and you can build, validate, and launch responsibly. A track record of leading ambiguous, cross-team work and delivering measurable outcomes (not just insights). Excellent executive communication and influence: you earn trust, align stakeholders, and move decisions forward. Strong product ... (truncated, view full listing at source)