Senior Data Scientist

What You'll Accomplish

• Build the measurement and experimentation foundation for HingeSelect's Visits and Discovery experience — 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.

Basic Qualifications

• 5+ years of experience in Data Science, Product Analytics, or related fields.
• 5+ years of experience with Python and SQL, with the ability to transform complex data into actionable insights.
• Experience designing and analyzing A/B tests and other experimental frameworks.
• Proven ability to translate complex data into clear, actionable insights and communicate findings effectively to both technical and non-technical stakeholders.
• Experience building dashboards and reports for monitoring product or operational metrics using tools like Mode, Tableau, or similar.
• Experience with productionalizing data models and ETL pipelines.

Preferred Qualifications

• 1+ years of experience with machine learning and predictive modeling.
• Bachelor's or Master's degree in a relevant field (Data Science, Computer Science, Engineering, Statistics, etc.).
• Prior experience in healthcare, wellness, or digital health environments, particularly in fitness technologies or real-time feedback systems.
• Knowledge of cloud infrastructure and tools such as AWS, Databricks, and Airflow.