Senior Product Scientist (Marketing Intelligence)

Headspace

17h•$122,400 - $190,000•Remote

About The Position

We are seeking a Senior Product Scientist to own and maintain the marketing measurement foundation that powers Headspace's ability to make confident, evidence-based decisions about how we acquire and retain members at scale. This role sits at the intersection of product strategy, data science, and growth marketing, with a mandate to move Headspace toward a rigorous, durable incrementality testing capability that connects media investment to long-term member value. As a Senior Product Scientist, you will partner closely with Performance and Lifecycle Marketing, Growth, Engineering, and Data Science to support the design and execution of the cross-channel experimentation infrastructure, identity resolution architecture, and measurement systems required to understand the true causal impact of our marketing spend. At Headspace, product leaders are expected to model experimentation with purpose, share feedback with care, and bring a deep commitment to the member journey. This role requires both scientific rigor and strategic clarity to translate complex measurement challenges into product decisions that drive sustainable, high-quality member growth.

Requirements

3 years of experience in product management, data science, marketing technology, or a related field, with a proven track record of owning and delivering complex, analytically driven products or workstreams.
Strong understanding of marketing measurement concepts including incrementality testing, media mix modeling, multi-touch attribution, and the technical infrastructure required to support cross-channel experimentation at scale.
Demonstrated experience partnering with data science or engineering teams to define requirements, build data pipelines, and ship measurement systems in production environments.
Solid grasp of identity resolution, event logging, and the data architecture considerations required to produce reliable, unbiased measurement across digital marketing channels.
Strong communication and interpersonal skills, with the ability to influence and align stakeholders across marketing, growth, engineering, and legal functions.

Nice To Haves

Experience working with customer data platforms, clean rooms, or similar data infrastructure used to support privacy-safe measurement and audience segmentation.
Familiarity with causal inference methods and their application to marketing measurement, including holdout group design, geo-based experiments, or synthetic control methods.
Prior experience in digital health, consumer subscription, or direct-to-consumer sectors where member LTV and retention are primary business drivers.
Experience navigating data privacy and consent requirements such as GDPR, CCPA, or ATT in the context of marketing measurement and experimentation.
A passion for mental health, evidence-based decision making, and the mission of Headspace.

Responsibilities

Craft product requirements & experimentation plans for marketing measurement, partnering with Performance and Lifecycle Marketing, Growth, and Data Science to build a durable, cross-channel incrementality testing capability that moves Headspace beyond last-click attribution.
Design and deliver the experimentation infrastructure required to measure the true causal impact of paid media spend on member acquisition, long-term retention, and sustainable LTV across all marketing channels.
Details and owns the outcome of the roadmap for data architecture and identity resolution foundation required to support reliable, scalable measurement, ensuring consistent signal across channels and reducing measurement bias and contamination.
Author and maintain product specifications that translate complex measurement and experimentation requirements into actionable technical deliverables for engineering and data science partners.
Partner cross-functionally across Marketing, Growth, Engineering, and Data Science to ensure execution aligns with established measurement principles, experimentation standards, and frameworks for evaluating and acting on marketing investment decisions.
Define and monitor the key metrics that connect platform-level measurement capabilities to business-level outcomes, including member acquisition quality, retention impact, and long-term LTV.
Maintain and iterate on a structured learning system that synthesizes incrementality findings across channels and over time, enabling leadership to make confident, evidence-based investment decisions at scale.
Partner with Legal and Privacy teams to ensure all measurement and experimentation infrastructure is designed and operated in full compliance with data privacy and consent requirements.