Senior Product Scientist, Headspace Health

• 3 or more years of experience in product science, data science, or a related field, with a proven track record of driving the execution of product features and systems in production environments
• Demonstrated experience shipping experiments to learn and iterating based on member signals, including the practical execution of marketing automation features or agentic AI prototypes within a dedicated cross-functional team
• Strong hands-on technical depth in SQL and Python, with the ability to build proofs of concept, contribute to data pipeline and feature engineering work, and engage as a credible technical partner to engineers and data scientists
• Deep understanding of lifecycle marketing execution, including experience integrating with Braze or similar CRM platforms via API to trigger personalized, event-driven communications at scale
• Solid grasp of paid media measurement and optimization including incrementality testing, media mix modeling, and the identity resolution and event logging infrastructure required to support cross-channel causal measurement
• Strong communication and interpersonal skills with the ability to influence and align with marketing, growth, and engineering partners on technically complex systems with real member and business risk

Desirable

• We are seeking a Senior Product Scientist to own and drive the execution of Headspace's agentic marketing system, a closed-loop growth engine that connects real-time member and campaign signals to evidence-based decisions about how we acquire and retain members at scale
• This is an execution-focused role sitting at the intersection of product strategy, data science, and growth marketing, with a mandate to deliver measurable results and move Headspace beyond static campaigns toward a system that perceives, reasons, and acts continuously across paid acquisition and lifecycle engagement
• The system has two interconnected layers
• On the lifecycle side, the agent monitors member behavioral signals in real time, selects the optimal intervention from a curated content and messaging library, and triggers personalized communications across push, email, and in-app channels without a human making each individual decision
• On the paid side, the agent monitors channel-level performance, reallocates budget across platforms, refines audience targeting, rotates and optimizes ad creative from a curated approved library, and surfaces gaps back to the marketing team when member acquisition needs exceed what existing audience strategies or creative assets can address
• Both layers depend on the same foundational infrastructure: clean identity resolution, reliable event logging, and a causal measurement layer that ensures every autonomous decision is rooted in true incremental impact rather than correlation
• We expect this person to routinely ship member-facing prototypes and lightweight experiments to drive learning and produces real member signals
• This role requires the ability to write SQL and Python, contribute to model evaluation, and be a proficient technical partner to engineers
• At the same time, this role requires clear communication of requirements and roadmap status to cross-functional teams
• At Headspace, product leaders are expected to model experimentation with purpose, share feedback with care, and bring a deep commitment to the member journey
• Own and drive the execution of the agentic paid media optimization system, managing the decision logic that monitors channel-level performance, reallocates spend across platforms, and ensures the efficient rotation of ad creative based on performance signals while communicating gaps to marketing partners
• Execute the agentic lifecycle marketing strategy, partnering with cross-functional teams on the implementation of decision policies, managing the curated action library of approved content, and ensuring the Braze integration triggers personalized communications within defined safety guardrails
• Manage data and feature requirements, partnering with Data Science and Engineering to ensure member-level behavioral signals and model outputs like churn risk and LTV are correctly utilized by the agentic systems to make reliable decisions
• Maintain autonomy and governance guardrails, executing budget checks, brand safety points, and audit logging to ensure the system operates within the rules defined by Legal, Privacy, and Clinical partners
• Implement causal measurement plans to connect agent decisions to member outcomes, focusing on distinguishing true incremental lift and providing data to improve decision policies based on measurable results
• Communicate clear product requirements and status to Engineering and Data Science partners, focusing on the execution of data pipelines, identity resolution, and model consumption patterns required for production
• Own the marketing measurement foundation including incrementality testing infrastructure, cross-channel attribution, and the identity resolution and event logging architecture required to produce reliable, unbiased causal measurement across paid and lifecycle channels
• Partner with Legal and Privacy teams to ensure all agentic systems are designed with GDPR, CCPA, and ATT requirements embedded from the start, and that every autonomous decision is auditable, explainable, and reversible