• Work independently with cross functional teams to develop software and system requirements. • Design, implement, and deploy state-of-the-art machine learning models. • Monitor the performance of the ML models and drive continuous improvement. • Lead team code quality activities including design and code reviews. • Communicate complex analytical findings and model performance metrics to both technical and non-technical stakeholders through clear visualizations and presentations. • Provide technical guidance to team members.

• Engaging with sales leads as a “human explicability layer,” up-leveling understanding for non-technical audiences to ensure that complex underwriting results are communicated clearly to drive sales. • Leading complex and dynamic collaborations between the Data Science and Growth teams. • Helping us refine the competitiveness of our value-based care contracts. • Ensuring quality and availability of various data sources. • Leveraging a deep understanding of ML concepts (e.g., using SHAP or feature importance but also less formal quantitative instinct) to consult on the application of payment models. • Designing and engineering analytics tooling to stress-test our underwriting outputs. • Deepening knowledge of healthcare models. • Evaluating vendors and technologies.

• Critically analyze large health datasets using standard and bespoke software libraries • Discuss your findings and progress with internal and external stakeholders • Produce high quality reports which summarise your findings • Contribute to research activities as we explore novel and established sources of re-identification risk

• Design, test, and optimize LLM prompts for conversational AI, text classification, and structured data extraction tasks. • Build evaluation pipelines to analyze prompt performance using quantitative metrics, human-in-the-loop feedback, and business KPIs. • Conduct prompt experiments and regression testing to ensure stability, accuracy, and safety as models evolve. • Collaborate with Machine Learning, Product, and Operations teams to translate business objectives into scalable, data-driven prompt-engineering strategies that enhance model accuracy, efficiency, and real-world usability. • Use Python/SQL to analyze model outputs, identify anomalies, and automate quality checks. • Document best practices and contribute to internal frameworks for prompt evaluation and continuous improvement. • Communicate findings effectively to technical and non-technical stakeholders, driving measurable business impact through insight-driven decisions.