About Headway
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
As a Senior Staff Data Scientist, Bayesian Experimentation and Causal Inference, you will be the company-wide owner of how Headway learns from data, especially when the stakes are high and the signal is noisy. You will report directly to the Head of Data and serve as a core leader for standards, frameworks, and decision quality across Product, Growth, Ops, and Finance.
Your work will set the default methods for how we answer questions like:
- Did this actually cause the outcome we care about?
- How sure are we, and what should we do given that uncertainty?
- What evidence is strong enough to change strategy, policies, or spend?
A major objective of this role is to build and institutionalize a clear map of “what we know” about patients, providers, and payers, with explicit confidence levels that tie directly to business action. Think of it as a shared language that prevents the organization from treating a correlation like a law of physics, while still moving fast.
What you will do
- Own causal inference and experimentation standards across Headway. Define the canonical approaches, guardrails, documentation, and review mechanisms for experiments and quasi-experiments, including when and how to use Bayesian methods.
- Build the confidence ladder for company knowledge. Create a clear, shared framework that maps findings to levels of confidence (for example 1–10), where lower levels reflect correlation and early directional evidence, mid levels reflect increasingly credible causal inference, and the highest levels reflect stable, repeatable, decision-grade truths. Operationalize it so it shows up in artifacts teams actually use: PRDs, launch reviews, growth planning, quarterly business reviews, and postmortems.
- Design the learning strategy for our hardest questions. Lead the approach for ambiguous, high impact domains like provider activation and retention, payer economics and policies, patient conversion and engagement, and marketplace dynamics. Recommend the right combination of randomized experiments, stepped rollouts, geo tests, natural experiments, and observational designs.
- Raise the organization’s statistical maturity. Introduce and standardize Bayesian experimentation practices where it improves speed and decision quality (priors, posterior interpretation, sequential decision rules, credible intervals, expected value framing). Build training, playbooks, and reusable tooling.
- Be the escalation point for difficult measurement problems. Tackle issues like interference and spillovers, network effects, selection bias, noncompliance, measurement error, multiple comparisons, seasonality, and Simpson’s paradox showing up in real life and causing confusion.
- Partner with Data Platform and Engineering to make rigor scalable. Ensure experimentation and inference are supported by instrumentation, logging, metric definitions, semantic layers, and monitoring. Help define the minimal foundations required for trustworthy learning.
- Build a culture of clear claims. Establish norms for separating facts, estimates, assumptions, and uncertainties. Make it easy for teams to say “we do not know yet” without losing momentum, and easy for leaders to understand what is safe to act on.
- Mentor and set the bar. Coach other data scientists and analytics leaders. Create review standards for causal work, support hiring for methodological depth, and represent Headway’s measurement philosophy internally and externally when appropriate.
What will make you successful
- 12+ years of experience applying causal inference, experimentation, and advanced statistics to real-world product, growth, or operational decisions (or equivalent depth demonstrated through scope and outcomes).
- Deep expertise in causal inference across randomized and observational settings, including practical strategy for when clean experiments are not possible.
- Deep expertise in Bayesian methods for experimentation and decision-making, and strong judgment about when Bayesian approaches outperform frequentist defaults and when they do not.
- Strong SQL and strong proficiency in Python or R, including building reusable analysis tools and improving team workflows.
- Track record of setting org-wide standards that materially improved decision quality and execution velocity.
- Executive-level communication and influence: you can drive alignment across Product, Growth, Ops, Finance, and Engineering.
- Comfort operating in ambiguity, and the ability to turn it into crisp frameworks, clear recommendations, and measurable outcomes.
- Motivation for our mission: improving access and affordability in mental healthcare.
Nice to have
- Experience in marketplaces, healthcare, insurance, or other regulated and complex incentive systems.
- Experience with experimentation under interference and network effects.
- Experience building experimentation platforms, analysis libraries, or statistical tooling used broadly across an organization.
- Familiarity with causal graphs, uplift modeling, and decision theory framing (expected value, value of information).