• Own the research and modeling work underlying Junction’s highest-priority data science opportunities across diagnostics, clinical workflows, and AI-enabled product features • Define rigorous frameworks for measurement, experimentation, and causal evaluation so we can distinguish signal from noise and make decisions we can defend • Lead development of predictive models, segmentation approaches, risk or routing logic, and other statistical systems that directly inform product and business strategy • Build the analytical foundation behind customer-facing features — from model development through to validation and performance tracking • Partner with engineering and data engineering to ensure models and analytical systems can be put in production, are reliable, and useful in real workflows • Establish how Junction evaluates data-driven and AI-enabled features, including methodology, quality thresholds, monitoring, and performance review • Communicate complex technical findings clearly to technical and non-technical stakeholders, including tradeoffs, limitations, and implications for action

• Responsible for leading and executes end‑to‑end data management activities • Ensure high‑quality, complete, and accurate information across diverse and complex projects • Maintain continuous quality improvement of data management-related processes • Keep abreast of changes in internal and external requirements • Develop and maintain processes for data management • Assist in development of data management-related processes • Proactively drives quality deliverables • Collaborate with internal and external stakeholders to ensure all data is complete, timely, and accurate • Work with cross-functional stakeholders to understand implications of updates • Lead discussions with healthcare organizations to educate on Pfizer requirements

• Utilize data analytics tools to support the design and implementation of claim fraud detection and prevention initiatives. • Compiles, cleans, organizes and analyzes complex data to understand fraud results. • Develop and maintain comprehensive data models to identify fraud patterns and trends. • Pilot technical solutions to enhance both operational and data analysis capabilities for fraud detection and prevention. • Collaborate with internal partners such as IT, Legal, Compliance, and Claims to ensure alignment and effective fraud mitigation solutions. • Design and operate reports to measure the effectiveness and loss cost containment of fraud mitigation tools, processes, and initiatives. • Build automated self-service reports to track referrals, assignments, performance metrics, and outcomes of claim fraud. • Develop a consistent communication strategy to keep all stakeholders informed and engaged. • Develop and maintain strong relationships with key departments to foster a culture of transparency and trust. • Ensure timely and effective communication with all relevant parties during fraud investigations.

• Responsible for designing evaluation frameworks, testing methodologies, and telemetry analysis for agentic systems, with working knowledge of LangChain and LangGraph to support and enhance engineering efforts. • Build, validate, and optimize data models focused on generative AI and large language models. • Create and refine methods to measure AI model and agent performance, quality, and impact. • Collaborate with product and engineering teams to inform and support integration of AI solutions (e.g., Gemini, OpenAI, Azure AI). • Stay current on advances in data science, machine learning, and generative AI. • Mentor junior data scientists and engineers. • Communicate clearly with stakeholders, presenting insights and recommendations. • Apply statistical, econometric, and machine learning methods to generate actionable insights and support decision-making. • Identify, integrate, and leverage key data sources to enhance analytics. • Develop scalable data structures and analytic products. • Lead end-to-end analytics and modeling projects, including data gathering, feature engineering, model development, evaluation, and monitoring. • Provide insights to encourage data-driven decision-making throughout the organization. • Mentor teams on data science best practices and contribute thought leadership through blogs or articles. • Develop and deliver training on analytics tools and data science techniques (e.g., Alteryx, Tableau).