Data Scientist - Marketing 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.

Join us to build the measurement and decision engine for patient growth.

As a Staff Data Scientist, Marketing Analytics, you will be the senior analytical and strategic leader who makes marketing performance legible, credible, and actionable. You will partner closely with Growth Marketing leadership and channel owners across paid, lifecycle, and organic, plus Finance, Product, and Engineering. Your job is to help Headway answer the questions that matter:

• What is truly incremental?
• Where should we invest next?
• What is driving performance shifts?
• How do we scale what works without fooling ourselves?

You will build the frameworks, analyses, and modeling approaches that enable the marketing team to move faster with confidence. This is high-stakes decision support for a growth engine that needs to compound and certainly not a dashboard-only role or “just attribution” role.

• Own incrementality measurement across channels. Design and analyze geo tests, holdouts, lift tests, and quasi-experimental approaches when randomized tests are not feasible. Define clear guardrails, decision rules, and what “good” looks like.
• Build a marketing measurement system that leaders trust. Define canonical metrics (CAC, LTV, payback, conversion, retention, capacity-adjusted ROI), ensure definitions are consistent, and create a clear measurement narrative that aligns Marketing, Finance, and Product.
• Turn ambiguity into a plan. When performance changes, you will diagnose why, quantify contributing drivers, and recommend concrete actions. You will be the person who can say, “Here’s what moved, here’s why we believe it moved, and here’s what we do next.”
• Develop and evolve modeling approaches where they create leverage. Build practical models such as LTV and retention forecasting, cohort value prediction, causal uplift models for lifecycle, and marketing mix modeling when appropriate. Focus on models that survive contact with reality: calibration, backtesting, and decision usefulness.
• Partner with Engineering on the measurement plumbing. Improve event instrumentation, identity resolution assumptions, offline conversion integration, and data quality monitoring so measurement is robust. Advocate for minimal, decision-critical requirements that unlock reliable learning.
• Design learning loops that scale. Create repeatable experimentation and analysis templates for channel and creative testing, including measurement of message by audience by surface. Increase testing velocity without lowering the truth standard.
• Influence strategy, not just reporting. Bring an evidence-based point of view on channel allocation, growth constraints, saturation, diminishing returns, and the tradeoffs between short-term acquisition and long-term retention and care outcomes.
• Uplevel the team. Mentor analysts and data scientists working on growth, set quality standards, and help establish best practices across experimentation, causal inference, and forecasting.

What Will Make You Successful

• 10+ years using data science,…