Our mission: eliminating every barrier to mental health.

Spring Health is a global mental health company on a mission to eliminate every barrier to mental health. We're building a world where getting support is simple, personal, and built around the person, so care can continue through every job, move, health plan, and life stage.

Our AI-native platform helps us deliver personalized support across self-guided tools, coaching, therapy, medication management, and specialty care. With outcomes independently validated by JAMA Network Open and the Validation Institute, Spring Health reaches more than 170 million people worldwide through leading employers, health plans, and partners.

As an AI-native company, we believe technology should expand the reach, quality, and humanity of care. Every Spring Health team member is expected to use AI tools thoughtfully, apply human judgment to AI outputs, and keep building AI fluency in ways that support their role and our mission.

The Senior Product Manager, Data Profiles will lead strategy and roadmap for the Data Profiles team in Spring Health’s R&D organization. The team is building the data foundation for personalized care. It has three jobs: give other teams one common way to access member data in production, turn raw data into useful personalization signals, and decide how external data sources should feed into that picture. This is a full-time role reporting to the Group Product Manager, Care Orchestration that is fully remote. You’ll work closely with data scientists, software engineers, and clinical and business stakeholders.

What you’ll do

Own the vision, strategy and roadmap for Data Profiles across its three areas: production data access, personalization signals, and external data integration.

Partner with software engineers to define and ship a common API layer that lets product teams reliably access member data in production to personalize experiences, and drive its adoption across Spring.

Partner with data scientists to find, test and put into production the personalization signals that come from data across our warehouse and production systems. Set clear hypotheses, success metrics and quality bars for each one.

Develop and run a strategy for evaluating, choosing and integrating external data sources.

Run continuous discovery with internal teams that use Data Profiles (product, engineering, clinical, data science) and turn their needs into one prioritized roadmap. Communicate tradeoffs and progress clearly to stakeholders at every level.

Set standards for profile data (definitions, freshness, accuracy, governance) together with clinical, legal, privacy and security partners, so member data is trustworthy, reusable and used responsibly.

Measure and report how the platform affects downstream teams and member outcomes, and use that data to decide where to invest next.

What success looks like:

Product teams increasingly use Data Profiles as their standard way to access member data for personalization, and it takes less time and effort to launch personalized experiences.

New personalization signals move from exploration to production on a regular schedule and measurably improve member engagement, care matching or outcomes.

The platform meets agreed targets for reliability, response time and data freshness.

How you’ll use AI in this role:

Use AI tools every day to speed up discovery, synthesis, writing specs and analysis.

Use AI to test hypotheses quickly before bringing in data science or engineering resources.

What you’ll bring

4+ years of product management experience, including 2+ years on data, platform or API products used by internal or external developers.

Experience shipping data products into production: data pipelines, feature or profile stores, customer data platforms (CDPs) or personalization systems.

Strong data and ML fluency: you can work with data scientists on feature engineering, signal quality, model evaluation and experiment design.

The technical depth to engage with engineers on API design, data models, latency, scalability and system tradeoffs.