Data Science Manager

Hinge Health
San Francisco-HQPosted 9 March 2026

Job Description

Data Science Manager Beware of Phishing Attempts: We've noticed an increase in phishing where fraudsters impersonate employees and send fake job offers to steal sensitive information. We'll never ask for financial details during the hiring process and only use "@hingehealth.com http://hingehealth.com" emails. If you receive a suspicious offer, stop communication and report it to the US FBI Internet Crime Complaint Center. To verify an email from our recruiting team, forward it to security@hingehealth.com. ABOUT THE ROLE At Hinge Health, we are building a "coherent platform" that seamlessly integrates digital and hybrid care to move people beyond pain. We are seeking a Manager, Data Science to lead the data strategy for two critical areas of our business: Treatment Experience and Hinge Select. In this role, you will own the data roadmap for our most innovative clinical technologies—including Computer Vision (Motion Insights), the Enso wearable device, and our Rewards & Insights (RAIN) engagement platform. Additionally, you will lead the analytics strategy for Hinge Select, our fast-growing hybrid care offering that connects members with virtual specialists and in-person providers. WHAT YOU'LL ACCOMPLISH - Strategic Thought Partnership: Serve as the primary data partner for the Treatment Experience and Hinge Select leadership teams. You will move beyond "service-desk" analytics to proactively identify opportunities (e.g., how to increase CV adoption, optimize Enso utilization, or improve Hinge Select funnel conversion). - Team Leadership & Development: Manage, mentor, and grow a team of Data Scientists (currently 4 direct reports). Foster a culture of technical excellence, autonomy, and "radical candor." You will be responsible for their performance management, career growth, and project prioritization. - Product Data Quality & Infrastructure: Drive the "Product Data Quality" initiative for your domains. You will oversee the definition of metric frameworks, enforce rigorous instrumentation/taxonomy standards (Mixpanel), and ensure the delivery of trusted, documented dbt data models in Databricks. - Experimentation Excellence: Uplevel the rigor and velocity of experimentation within your pods using Statsig. You will guide your team in designing complex experiments (e.g., triggering logic for CV assessments, Enso onboarding flows) and interpreting results to prevent "shipping to learn" without clear hypotheses. - 0-to-1 Product Analytics (Hinge Select): Establish the foundational data architecture for Hinge Select. You will work with Engineering to solve complex data ingestion challenges (bridging internal Postgres, 3rd-party claims, and EHR data) to build the first comprehensive view of our hybrid care supply and demand. - Cross-Functional Collaboration: Bridge the gap between technical data work and business outcomes. You will ensure your team’s work—whether it’s a predictive model for M2M referrals or a dashboard for weekly streak adoption—is actionable, accessible, and aligned with company OKRs. WHO YOU ARE - A Player-Coach: You thrive in both leading a team and staying close to the technical work, balancing 0-to-1 product launches with the optimization of established technologies. - A "Learn-it-all": You constantly test new ideas and are willing to learn and get better, fostering a culture of technical excellence and experimentation. - A Trust Builder: You communicate complex technical concepts effectively to non-technical executives and partner closely with Product, Engineering, and Design leadership. - Action-Oriented: You bridge the gap between technical data work and business outcomes, ensuring insights are actionable and aligned with company goals. BASIC QUALIFICATIONS - Bachelor’s degree in Computer Science or related field, or equivalent professional experience. - 8+ years of experience in Data Science, Product Analytics, or a related technical field. - 2+ years of people management experience (or 2 ... (truncated, view full listing at source)
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