Senior Data Scientist

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 90,000 providers across all 50 states run their practice on our software, serving over 2.6 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, and building much of it with AI. 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 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.

We’re looking for people who like ambiguity, enjoy hard problems, and want their work to matter in the world.

What you will do

• 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.

• 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.

• Develop predictive models that operationalize vetted insights (feature development, validation, backtesting, calibration), with clear launch criteria and monitoring plans.

• Produce analysis and narratives that are crisp, honest about uncertainty, and drive action.

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

What will make you successful

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

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

• Depth in experimentation and causal inference under real-world constraints.

• Fluency with AI tools as part of your workflow: using LLMs and coding assistants to accelerate analysis, prototyping, and documentation, while still owning the judgment and rigor behind the output.

• 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.

Where you could land

Data Science at Headway is structured around three core pillars, each focused on a distinct area of the business. Within each pillar, smaller pods own specific problem spaces, and several of those pods are actively hiring. We keep our interview process consistent across teams, and ahead of your preliminary call we’ll share a pre-read with details on the specific pods that are hiring so we can talk through where you might fit best.

• Patient: The Patient group owns how someone finds mental health care on Headway and gets to a provider who fits them: the first visit or phone call, search and matching, booking, and then attending a first appointment and staying in care. Data Science here means consumer funnel analytics, experimentation, and marketing science. That includes defining the metrics the group steers by, designing and reading experiments, and building causal and attribution methods for channels where you can't cleanly randomize. The modeling work spans ranking, matching, propensity, and the lifetime value and attribution models behind how we spend to acquire patients.

• Payer & Outcomes: The Payer & Outcomes group is responsible for driving best-in-class patient outcomes, building a trustworthy and safe provider network, and delivering value-based care with responsible costs relative to outcomes, both through product work across the company and by working directly with payers. On the Data Science side, there's room to go deep on product data science, commercial data science, or a mix of both: experimentation, causal modeling, forecasting, and sizing work. People are often drawn to this team by its rapid growth and by how directly the day-to-day work ties back to Headway's mission.

• Provider: Providers have real choices about where they build their practice. A provider might join Headway for credentialing, stay for reliable payments and tools that make the day easier, find new clients or bring over existing ones, or grow into a group practice. The Provider group owns that relationship and the trust it depends on, because we believe a clinician who feels supported can better help their patients. Data Science here is about understanding those choices: what draws a provider in, what keeps them, and what helps them grow with us. Experiments usually run at the provider level and often pair a product change with a conversation. Causal inference fills the gaps where randomization isn't possible. The models cover lifetime value, attribution, churn, and how providers respond to rates and incentives, along with work on how our tools support providers and their patients. Because the work spans product and go-to-market, Data Scientists here see the full provider relationship.

Compensation

The expected base pay range for this position is $180,000-225,000, based on a variety of factors including qualifications, experience, and geographic location. In addition to base salary, this role may be eligible for an equity grant, depending on the position and level.

Comprehensive benefits:

Medical, dental, vision, HSA/FSA, 401(k) including match for 2027!, Carrot Fertility reimbursement, and paid parental, medical and caregiver leave.

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