This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are hiring a Clinical Data Scientist to lead the generation of credible, clinically rigorous evidence demonstrating how our program improves member health and long term outcomes. This role is explicitly clinical. You will design and execute outcome analyses that inform clinical decision making, support employer and partner credibility, and strengthen the foundation of our care model. Key Responsibilities - Clinical Impact: - Design and analyze clinical outcome studies evaluating weight loss and durability, medication adherence, metabolic and cardiometabolic risk indicators, and safety or clinical escalation patterns. - Build analyses that define the right cohorts, starting points, and follow up timelines to measure program impact. - Partner closely with Clinical leadership to evaluate treatment pathways, protocol adherence, and the timing and intensity of clinical touchpoints. - Assess the impact of clinical interventions on outcomes and overall member experience. - Use insights to inform and continuously improve clinical protocols and program design. - Contribute to building internal evidence frameworks that define and measure clinical effectiveness within our care model. - Translate analytic findings into clear, practical insights that support clinical and operational decision making. - Data & Analytics: - Ensure analyses align with accepted clinical standards and are appropriate for clinical leadership, employer, and partner audiences. - Produce documentation and outputs that can withstand external scrutiny and support potential presentations or publications. - Partner with Data Engineering to define clinical data requirements, quality standards, and analytic definitions. - Ensure clinical analyses are reproducible, auditable, and clearly documented. - Work cross-functionally with Clinical and Product teams to strengthen the integrity, credibility, and usability of our outcomes data. Qualifications - 5+ years of experience analyzing clinical or healthcare outcomes data. - Experience analyzing data over time and working with cohort based analyses. - Proficiency in SQL and Python or R. - Proficiency with Looker preferred. Requirements - The salary range for this role is $110,000-140,000. Benefits - Enjoy a generous paid time off policy, including multiple paid company holidays, wellness days, and floating holidays to support your work-life blend. - Medical, dental, and vision benefit options to keep you and your family healthy. - Calibrate-funded disability and basic life insurance, ensuring peace of mind during unforeseen events. - Access to several wellness programs, including a complimentary Headspace membership, and therapy on your schedule with Headspace Care. - Employee Assistance Program through Prudential to receive counseling on a wide range of topics. - Remote-first ways of working, with the flexibility to work from any state. - Competitive paid parental leave program to support new parents.

• Lead full-lifecycle data science projects, from conceptualization to deployment and ongoing maintenance. • Elevate our standards on the data science team when it comes to operational excellence. • Collaborate with our operations, engineering, and service delivery teams to ensure seamless integration and management of our machine learning capabilities. • Communicate complex data science concepts effectively to a diverse range of stakeholders so we get the outcomes we want and capture learnings along the way. • Use your expertise to find simple, effective solutions when advanced models aren’t required. • Engage in continuous learning and sharing, fostering an environment of collective growth and innovation across departments. • Bring clarity and understanding to complex data science projects. • Foster a culture of collaboration and continuous improvement. • Champion our values of curiosity, adaptability, and teamwork.

• Write production code in Python. • Design, launch, and analyze experiments to optimize ad campaigns. • Build reporting and analytics tools for the Data Science Team's customers. • Translate into tvScientific's Data Science Product.

• Production-Grade Modeling: Design, develop, and maintain reproducible machine learning models for production environments. • Behavioral Forecasting: Build advanced models to predict user engagement patterns, including retention and activity frequency. • Optimization & Causal Inference: Develop Uplift models to measure the incremental impact of business interventions. • Recommendation Systems: Improve and scale next-best-action (NBA) engines to enhance personalized user experiences. • Experimental Design: Lead the statistical design and analysis of A/B testing to validate model performance and business hypotheses.