• Analyze unstructured and semi-structured data using advanced computational methods • Develop and implement algorithms for large-scale data analysis on distributed and cloud-based infrastructures • Process and index high-volume data collections and high-velocity data streams • Utilize advanced tools to interpret, connect, predict, and derive insights from complex data • Apply machine learning, algorithm analysis, and data clustering techniques • Support software development using open-source and enterprise technology stacks (Java, Linux, Ruby, Python, .NET, C#, C++)

• Understand complex business challenges and translate them into data science problems. • Explore, analyze, clean, and model structured and unstructured datasets from various sources. • Design and implement machine learning models, statistical tests, and optimization strategies. • Conduct experimentation using A/B testing, uplift modeling, and simulation frameworks. • Deliver proofs of concept and production-ready models within Agile, cross-functional teams. • Create and present data-driven storytelling insights for both technical and business stakeholders. • Contribute to model explainability, fairness, and responsible AI practices. • Stay current with academic and industry research and promote innovative solutions.

• Develop and tune generative/agentic AI solutions aimed to solve highly complex problems within the delivery of healthcare or health plan services. • Establish comprehensive tracking of experiments to manage the iterative process of building and testing models ensuring that AI solutions fulfill business requirements. • Provide statistical rigor to these evaluations via the design of controlled tests to measure the impact of changes to the solution. • Form and leverage strong collaborations with AI engineers to scale solutions for production grade performance. • Develop and implement complex agentic workflows that utilize AI agents for autonomous task execution, enhancing operational efficiency and decision-making capabilities. • Solve new business problems by reaching, designing, building, and validating complex or novel machine learning models. • Conduct feature engineering and model optimization. • Manage the complete lifecycle of machine learning models from conception to deployment while following SDLC and responsible AI practices. • Consult with senior and executive level business leaders to scope, model, and recommend AI/ML, technical, or analytic solutions to highly complex problems within healthcare. • Build and ensure consensus with stakeholders regarding the feasibility, tradeoffs, and delivery of recommended solutions. • Research, recommend, and apply causal techniques (RCT, DiD, PSM, causal graph models, counterfactual reasoning, etc.) to estimate treatment effects to quantify or validate business benefit of complex health plan activities. • Explain business impact to program owners and leadership using approaches that are meaningful to those audiences. • Partner with program owner in the promotion or publications of results when applicable. • Participate in code peer reviews and quality assurance testing. • Troubleshoot issues as they arise to solve problems independently and collaboratively. • Serve as the technical subject matter expert to establish best practices for the team and help coach the team via formal and informal initiatives. • Maintain regular contact with customers through development cycle to ensure each step of implementation tracks customer’s needs. • Lead analytics projects applying a working knowledge of Agile and SCRUM project methodologies.

• Leads and manages the entire lifecycle of data science projects • Collaborates with cross-functional teams to define project scope, objectives, and success metrics • Ensures projects align with organizational goals and deliver measurable impact on healthcare outcomes • Leverages deep understanding of machine learning algorithms to build sophisticated predictive models • Utilizes clustering, dimension reduction, and deep generative models to uncover hidden patterns • Applies rigorous validation techniques to ensure model accuracy, reliability, and fairness • Oversees the deployment of models into production environments • Extracts insights from clinical and operational data sources to inform decision-making • Translates complex technical findings into compelling narratives for non-technical stakeholders • Facilitates data-driven decision-making by communicating the value and impact of AI models • Mentors and guide junior data scientists • Promotes collaboration, knowledge sharing, and continuous learning • Contributes to developing best practices and standards for data science