Director of Data Science, Headspace Health

• 12+ years of experience in data science, product science, marketing science, quantitative analytics, experimentation, economics, statistics, machine learning, or related fields
• Proven ability managing senior ICs and people managers, including managers and principal-level scientists
• Deep expertise in experimentation, causal inference, and predictive modeling
• Experience partnering closely with Product and Engineering on instrumentation, measurement strategy, and decision-making
• Strong track record influencing roadmap and investment decisions with clear, evidence-based recommendations
• Demonstrated ability to connect behavioral measurement and scientific work to business outcomes and product strategy
• Excellent communication and executive-facing partnership skills

Desirable

• This leader will manage a team that includes managers and principal-level scientists and will be accountable for both the technical quality of the work and the leadership systems that make strong science repeatable at scale
• The role requires someone who can operate at multiple altitudes: setting strategy, coaching senior leaders, partnering with executives, and staying close enough to the work to push quality on experiment design, causal reasoning, measurement choices, segmentation, and modeling
• Success in this role requires owning outcomes end-to-end—ensuring that scientific work not only meets a high technical bar, but also drives clear decisions, measurable impact, and meaningful improvements in member and partner experiences
• Own the strategy, execution, and impact of Data Science across product, marketing, and forecasting
• Set direction for how the team supports product development, experimentation, lifecycle, growth, and business decision-making
• Ensure focus on the highest-value problems across Product, Marketing, Care, and B2B, tied to member and business outcomes
• Establish high standards across experimentation, causal inference, modeling, forecasting, segmentation, and statistical rigor
• Raise quality in hypotheses, metrics, experiment design, model evaluation, and decision recommendations
• Build durable review mechanisms and operating standards to ensure work is sound, interpretable, and decision-ready
• Lead product analytics to shape how user behavior is understood across acquisition, engagement, retention, and care journeys
• Strengthen data foundations including instrumentation, telemetry quality, validation, and experimentation readiness
• Partner cross-functionally to define measurement strategies that demonstrate care impact, outcomes, and ROI
• Build and lead a high-performing team by developing leaders, raising the talent bar, and leveraging AI to improve productivity and insight generation