Staff Data Scientist - Product Analytics

1 in 4 people in the US have a treatable mental health condition, but most providers don't accept insurance, making therapy too expensive for most people. Headway’s mission is to fix this by building a new mental healthcare system everyone can access. We started by solving the biggest barrier to care: insurance. The admin work - credentialing, claims, payment reconciliation - is a nightmare. We've automated that.

But we're going further. Over 75,000 providers across all 50 states run their practice on our software, serving over 1 million patients. We are building the best tools for therapists to run their entire practice, reimagining the experience of finding a therapist, and investing in the platform foundations to enable this at scale. We aren't just a billing layer; we are becoming the platform where care actually happens.

We're a Series D company with $325M+ in funding (a16z, Accel, Spark Capital, etc.), looking for exceptional people to help us achieve this mission. 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 sense and an instinct for decision leverage: you focus on what changes outcomes, not what is merely interesting.