Lead Data Scientist

The Lead Data Scientist will independently lead applied AI and data science workstreams that translate complex healthcare and underwriting data into measurable improvements in risk selection, pricing accuracy, operational efficiency, and the underwriting experience. The role will personally lead work from problem framing, target definition, feature engineering, and model development through rigorous validation, production deployment, monitoring, and business-impact measurement.

The Lead Data Scientist will operate with broad autonomy, manage workstream plans and risks, and bring major methodological, governance, or cross-platform decisions to the VP of AI and VP Analytics when a decision has broader business impact.

In addition to delivering models, the Lead Data Scientist will help build internal AI products and contribute improvements to shared practices for temporal validation, explainability, responsible AI and data use, model governance, and performance monitoring. Working with the VP of AI and VP of Analytics, the role will align assigned workstreams to the broader scientific roadmap and escalate decisions with cross-workstream impact. The role will partner with Business, AI, and Engineering to translate complex evidence into clear recommendations and measurable business outcomes.

• Own end-to-end delivery and measurable outcomes for assigned predictive underwriting and pricing workstreams, from problem framing through deployment, monitoring, and continuous improvement, including:
• Frame evidence-based recommendations and workstream trade-offs across model quality, risk, cost, scalability, aligning partners on delivery and measurable outcomes
• Defining and documenting the Analytics-Ready Dataset, data-quality, and reusable feature requirements needed for assigned workstreams across claims, pharmacy, utilization, financial, underwriting, and external data
• Applying rigorous point-in-time development and out-of-time validation approaches for claims maturity, seasonality, leakage, stability, and uncertainty
• Developing, comparing, and challenging predictive models, distributions, and hybrid rule/model approaches based on evidence and operating constraints
• Balance near-term delivery with disciplined exploration of emerging methods, including deep learning or GenAI solutions, that can materially improve accuracy, scalability, decision quality, or operating efficiency
• Translating model needs into detailed feature requirements; partnering with business teams to identify additional signals and with Legal to secure approvals
• Managing third-party model evaluations and ROI analyses when external expertise or independent validation is needed
• Producing explainable, reproducible model outputs and following shared documentation, testing, monitoring, retraining, and rollback standards while recommending improvements based on workstream experience