Staff Data Scientist - Growth

What you will do

• 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, diagnose why, quantify contributing drivers, and recommend concrete actions.
• 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.
• Design learning loops that scale. Create repeatable experimentation and analysis templates for channel and creative testing, including measurement of message by audience by surface.
• 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, analytics, and experimentation to drive decisions in marketing, growth, or marketplace environments (or equivalent scope and demonstrated impact).
• Deep expertise in causal inference and incrementality in real-world marketing systems: you know the failure modes (selection bias, channel cannibalization, platform noise, attribution myths) and how to design around them.
• Strong SQL plus strong proficiency in Python or R, with the ability to build reliable, reusable analytical workflows.
• Practical modeling skill, especially as applied to marketing and growth: cohorting, forecasting, LTV estimation, saturation and diminishing returns, MMM concepts, calibration and monitoring.
• Track record of influencing executive decisions with clear recommendations and measurable outcomes, not just analysis.
• Excellent communication: you can make complex measurement logic understandable and defensible to non-technical partners, and you can call out uncertainty without losing momentum.
• High ownership and strong judgment: you prioritize what changes decisions, you move quickly, and you know when to slow down because the risk is real.
• You are motivated by the mission. Access and affordability in mental healthcare are not abstract problems here.

Nice to have

• Experience with geo experiments, marketplace constraints, or capacity-aware marketing optimization.
• Experience measuring acquisition quality beyond conversion: downstream engagement, retention, clinical matching quality, and unit economics.
• Familiarity with lifecycle marketing measurement (incrementality, uplift, experimentation design for messaging).
• Experience partnering with Finance on budget allocation, payback, and scenario planning.
• Comfort working with imperfect identity, privacy constraints, and evolving attribution ecosystems.