Lead Data Scientist

To drive measurable improvements in risk selection and pricing accuracy, the full-time remote Lead Data Scientist will independently manage applied AI and data science workstreams, overseeing the entire process from problem framing to model deployment and performance monitoring. Key Responsibilities: Own end-to-end delivery and measurable outcomes for predictive underwriting and pricing workstreams, ensuring alignment on model quality and operational efficiency Define and document data requirements, ensuring high data quality and readiness for analytics across various data sources Develop, validate, and deploy predictive models while championing best practices in explainability, model governance, and performance monitoring Required Qualifications: Bachelor's or master's degree in Statistics, Data Science, Computer Science, Mathematics, Engineering, or a related quantitative field; an advanced degree is a plus 8+ years of experience in data science, machine learning, or actuarial analytics, with a focus on healthcare or insurance risk Advanced proficiency in Python and SQL, with experience in machine learning frameworks such as scikit-learn and XGBoost Proven experience managing production models and the MLOps lifecycle on cloud platforms like AWS Strong business acumen with the ability to connect analytical outputs to measurable business outcomes