Senior Product Scientist (Care Intelligence)

Headspace

13h•$122,400 - $190,000•Hybrid

About The Position

We are seeking a Senior Product Scientist to own the measurement systems and data infrastructure that power Headspace's ability to deliver effective, efficient care at scale. This role sits at the intersection of product strategy, data science, and care operations, with a mandate to bring scientific rigor to two of our most consequential problem areas: how we forecast coaching demand and how we demonstrate the value Headspace delivers to members, employers, and health plans. As a Senior Product Scientist, you will partner closely with Data Science, Care Operations, and Clinical leadership to redesign a forecasting system that directly drives staffing, capacity, and cost decisions across our coaching network, while simultaneously building the evidence framework that allows Headspace to tell a credible, compelling ROI story to every audience we serve. 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 analytical depth and strategic clarity to translate complex modeling and measurement challenges into product decisions that meaningfully improve care delivery for our members.

Requirements

3 to 5 years of experience in product management, data science, or a related field, with a proven track record of owning and delivering complex, analytically driven products or workstreams.
Strong analytical skills and comfort working with quantitative modeling, forecasting systems, or measurement frameworks, with the ability to translate technical complexity into clear product decisions and stakeholder communication.
Demonstrated experience partnering with data science or engineering teams to define requirements, validate models, and ship data-powered products in production environments.
Exceptional strategic thinking skills, capable of identifying the highest-leverage problems, building a clear point of view, and rallying cross-functional partners around a shared plan.
Strong communication and interpersonal skills, with the ability to influence and align stakeholders across product, operations, clinical, and commercial functions.

Nice To Haves

Experience in digital health, clinical operations, workforce planning, or enterprise wellness sectors.
Familiarity with forecasting methodologies, causal inference, or health economics and outcomes research.
Experience building or scaling measurement and reporting infrastructure, including claims analytics, survey systems, or outcomes tracking.
A passion for mental health, evidence-based care, and the mission of Headspace.

Responsibilities

Craft and deliver against the product roadmap for care demand forecasting, collaborating with Data Science and Care Operations to continuously improve models that translate expected member volume into accurate staffing, capacity, and cost decisions across our care network.
Author and maintain comprehensive Product Requirements Documents (PRDs) that translate complex forecasting and measurement requirements into actionable technical specifications, including model architecture decisions, validation frameworks, and operational integration requirements.
Provide technical product oversight and coordinate dependencies with Data Science and Care Operations to ensure alignment on forecasting methodology and operational implementation.
Define and monitor key technical metrics (e.g., forecast accuracy, model reliability) to ensure models are continuously improving and driving measurable efficiency gains across the care network.
Build and maintain a structured validation and learning loop that systematically compares model performance against projections, enabling rapid recalibration and continuous improvement.
Develop and implement the measurement framework for ROI, focusing on the evidence infrastructure and reporting capabilities needed to track Headspace’s value across member and operational outcomes.
Partner with Clinical, Commercial, and Data Science teams to close measurement gaps and build a credible, scalable value story across key member outcome, workforce, and payer metrics.