Staff Data Scientist

Staff Data Scientist About the Role Hinge Health is expanding from digital PT into full musculoskeletal care through HingeSelect, which enables members to self-select into in-person visits, specialist referrals, and other care pathways. As we scale this offering, we need a Staff Product Data Scientist to build the measurement and experimentation foundation that turns product investment into measurable growth. This is a 0-to-1 role. You will be the first dedicated data scientist on HingeSelect's discovery and visits experience, partnering directly with Product and Engineering to diagnose low conversion, establish causal guardrails against cannibalization of existing programs, and ensure strong instrumentation so every product bet has a clear feedback loop to business impact. You will also model how provider capacity and member demand interact to optimize supply-demand dynamics across the care network. The ideal candidate is deeply technical, fluent in AI-assisted development, and comfortable defining end-to-end data architecture from scratch. You should be able to write production-quality code using AI tools, design experiments that hold up to causal scrutiny, and translate ambiguous business questions into rigorous analytical frameworks. Without this role, analytics remain superficial, key strategic risks go unmeasured, and engineering teams ship without a clear path from feature to outcome. Our stack: Python, SQL, dbt, Mode, Databricks, Airflow, Statsig, AWS What You'll Accomplish - Build the measurement and experimentation foundation for HingeSelect end-to-end — from instrumentation design through causal analysis and metric reporting. - Diagnose conversion bottlenecks across the HingeSelect funnel (discovery, selection, scheduling, visit completion) and partner with Product to prioritize highest-leverage interventions. - Design and execute experiments with causal guardrails to measure HingeSelect's incremental impact while detecting and preventing cannibalization of existing digital PT engagement. - Model supply-demand dynamics — how provider capacity, geographic coverage, and member demand interact — to inform network strategy and capacity planning. - Define the data architecture for HingeSelect from scratch: event taxonomy, data models, and metric definitions that enable reliable downstream analysis and AI agent consumption. - Build and maintain dashboards and self-service tools that give Product, Engineering, and leadership real-time visibility into funnel performance and experiment results. - Advocate for upstream instrumentation best practices, working with Engineering to ensure events are captured server-side with consistent schemas and timestamps. - Apply machine learning and predictive modeling to forecast member behavior, segment demand, and identify high-value intervention points across the care selection journey. - Write production-quality code using AI tools (Claude, Cursor, Copilot) to accelerate development of data models, analyses, and pipelines. Basic Qualifications - Bachelor's or Master's degree in a relevant field (Data Science, Computer Science, Economics, Statistics, Engineering, etc.). - 7+ years of experience in Data Science, Product Analytics, or related fields. - 7+ years of experience with Python and SQL, with the ability to build end-to-end analytical solutions. - Experience designing and analyzing A/B tests and causal inference methods (e.g., difference-in-differences, instrumental variables, synthetic control). - Experience defining metrics, building data models, and establishing measurement frameworks in ambiguous, greenfield environments. - Experience with productionalizing data models and ETL pipelines. - Experience with AI-assisted coding tools and ability to leverage them for rapid prototyping and production development. Preferred Qualifications - 2+ years of experience with machine learning and predictive modeling, particularly in funnel optimization or marketplace/ ... (truncated, view full listing at source)