Sr. Product Manager - Recommendation Systems

We are seeking a Senior Product Manager to own product strategy for Recommendation Systems for health and wellness solutions and the engine that decides what guidance, coaching, and content each user sees, and when they see it. Reporting to the Head of Behavioral Science, Coaching, and Program Design, this role sits at the center of the product: partnering with Applied Behavioral Science on decision logic, with Decision Science on causal inference, and with AI/ML Engineering on ranking, personalization, and system architecture, to turn model and behavioral-science outputs into a recommendation experience people trust and act on.

You will own the roadmap for ranking, personalization, and the recommendation surface end-to-end — defining product strategy, prioritizing opportunities, designing experiments, and shipping outcomes that move engagement, retention, and real health-behavior change. This is a high-ownership, high-visibility role for someone who can operate independently in ambiguity, translate technical and behavioral complexity into a clear roadmap, and align Engineering, ML, Design, Behavioral Science, and Content around a shared bet.

This is a high-leverage, intellectually demanding role for a senior product manager who combines strong technical fluency in ML/recommendation systems with genuine curiosity about behavior change, and who is comfortable operating at an industry pace across a system that is still being built.

Product Strategy & Roadmap

• Own the Recommendation Systems Roadmap: Define and own the product strategy and roadmap for personalization and recommendation deliver, prioritizing bets that move engagement, retention, and health-behavior outcomes.
• Translate Model & Behavioral Outputs into Product: Partner with AI/ML and Behavioral Science to turn decision-logic outputs, ontology structure, and model capabilities into a coherent, shippable recommendation experience.
• Identify and Prioritize Opportunities: Evaluate new personalization and recommendation opportunities across the app and user journey — and build a prioritized pipeline tied to company-level goals.
• Define Product Requirements: Write clear requirements, specify tailoring and delivery constraints (cadence, cooldowns, sequencing) and set success criteria for every initiative.
• Own Launch Readiness: Drive the recommendation system toward launch, sequencing dependencies across Engineering, ML, Science, and Product, ensuring instrumentation and guardrails are in place before release.

Experimentation & Measurement

• Lead KPI Definition and Defense: Propose primary and supporting metrics for every initiative, and prevent metric drift by proactively aligning stakeholders on what “working” means.
• Own Experiment Design End-to-End: Partner with Behavioral Science on micro-randomization, adaptive trial designs and in-product experimentation; ensure every test is tied to a concrete product decision.
• Drive Experiments to Decisions: Turn experiment results into roadmap changes, not just readouts — ensuring rigor doesn’t come at the expense of shipping speed.
• Monitor Production Performance: Track recommendation quality and engagement in production, flag regressions or drift, and work with ML and Science to correct course quickly.

Cross-Functional Leadership

• Partner with AI/ML Engineering: Work directly with ML engineers on ranking, retrieval, and recommendation architecture, engaging meaningfully in technical trade-off discussions.
• Align Engineering, Design, and Content: Maintain lockstep with partner teams on scope, sequencing, and timelines, and keep stakeholders informed of progress, risks, and blockers with clear options and recommendations.
• Communicate with Clarity: Present strategy, trade-offs, and recommendations with high signal-to-noise, in writing and in person, to both technical and executive audiences.

• Education: BS/MS in a quantitative field (Computer Science, Engineering, Statistics, or related) preferred; MBA a plus.
• Experience: 7+ years in product management, including meaningful time owning recommendation, ranking, personalization, search, or feed products, ideally in consumer tech, health tech, or wellness.
• Recommendation & Personalization Systems: Working knowledge of ranking, collaborative filtering, contextual bandits, or content-based recommendation approaches, and familiarity with personalization at scale.
• Experimentation & Causal Rigor: Comfort with A/B testing, experiment design, statistical significance, and metric frameworks; ability to define, defend, and prevent drift on KPIs.
• Technical Depth: Proven ability to work closely with ML engineers and data scientists on model performance, feature engineering, and system architecture, and to make sound product trade-offs from technical constraints.
• Ownership and Bias to Action: Track record of driving ambiguous, cross-functional initiatives end-to-end — from problem framing through strategy to shipped outcomes.
• Cross-Functional Leadership: Demonstrated ability to influence and align engineering, data science, design, and content/behavioral teams without direct authority.
• Communication: Excellent written and verbal communication; able to write clear product specs and present strategy and trade-offs to both technical and non-technical audiences.
• Industry Knowledge: Experience in health tech, digital health, wellness, wearables, or consumer subscription products is strongly preferred.

• 100% covered Medical/Dental/Vision insurances for employee AND spouse + dependents!
• 401K with 4% employer match (eligible after 90 days of employment) and immediate 100% vesting
• Generous PTO policy + paid holidays
• Life Insurance
• Voluntary Life Insurance
• Disability Insurance
• Critical Illness Coverage
• Accident Insurance
• Healthcare FSA
• Dependent Care FSA
• Travel Assistance Program
• Employee Assistance Program (EAP)
• Fully stocked kitchen