Role Description Red Pulley Technology Solutions, Inc. is seeking a Health IT Data Scientist II to support the ICE Health Service Corps (IHSC) Health Information Technology Unit (HITU) Enterprise Health IT Staffing and Professional Services Support program. This role applies statistical, predictive, and exploratory analytic techniques to complex healthcare and operational data to generate insights that inform strategic planning, performance monitoring, and evidence-based decision-making. All development, configuration, and implementation activities performed by this role are conducted within authorized Government-managed platforms and environments, subject to Government oversight, direction, and approval, with written authorization required prior to deployment to production environments. This position follows a remote delivery model supporting IHSC/HITU, consistent with current program operations. - Location: Washington, DC Metro Area (Remote) - Clearance: Public Trust - Preliminary and Complete Fitness Determination Required - Employment Type: Full-Time - Status: Contingent Upon Contract Award Qualifications - Advanced degree or equivalent experience in Data Science, Statistics, or a related quantitative field - Advanced expertise applying statistical, predictive, and exploratory analytics to healthcare and operational data - Experience designing and implementing data models, forecasts, and analytic methodologies - Strong knowledge of healthcare data domains, including utilization, cost, quality, and performance metrics - Experience conducting cost modeling, trend analysis, and scenario forecasting - Skill in communicating complex analytic findings through reports, dashboards, and executive briefings Requirements - Experience collaborating with Data Engineers to operationalize and scale analytic models - Experience developing repeatable analytic frameworks and methodologies for enterprise use - Familiarity with federal healthcare data governance, privacy, and security requirements - Experience supporting high-priority, time-sensitive analytical requests for senior leadership Key Responsibilities - Design and execute advanced data analysis, predictive models, and statistical methodologies using healthcare and operational data - Perform cost modeling, utilization analysis, trend analysis, and forecasting to support strategic planning - Collaborate with Data Engineers to operate analytic models and ensure scalability - Develop repeatable analytic frameworks and methodologies for enterprise use - Validate data sources, analytic assumptions, and model outputs to ensure accuracy and reliability - Translate analytic results into actionable insights through reports, dashboards, and executive briefings - Support leadership requests for high-priority, time-sensitive analyses - Document analytic methodologies, assumptions, and limitations Benefits - Competitive compensation aligned with experience and market conditions - 401(k) retirement plan with company matching to support long-term financial security - Comprehensive health coverage, including medical, dental, and vision insurance - Health Savings Account (HSA) and Flexible Spending Account (FSA) options - Life insurance coverage for added peace of mind - Generous paid time off (PTO) to support work-life balance - Parental leave for growing families - Professional development assistance, including training and certification support - Tuition reimbursement to encourage continued education and career advancement

• Learning all about the core business of information exchange / release of information in healthcare • Diving deep with customers, sales, customer success, operations, product, data engineering, and business intelligence teams on key reporting, analytics, and productivity measurement needs • Developing a clear understanding of Datavant’s internal and customer-facing reporting experiences, including Healthsource, Provider Console, and other downstream applications where users consume reporting insights • Partnering with Data Engineering and Business Intelligence to clarify the operating model for reporting and data products, including how user needs become data requirements, reporting deliverables, dashboard experiences, and roadmap priorities • Creating and socializing a reporting and data product roadmap that balances customer reporting needs, internal operations needs, dashboard user experience improvements, and Healthsource productivity instrumentation • Identifying quick-win improvements to existing reporting experiences that help customers and internal teams better understand performance, productivity, status, and opportunities for improvement • Defining success metrics for reporting adoption, user satisfaction, data quality, dashboard usability, and operational productivity insights

• Separate durable preference from noise. Design robust feature representations from high-cardinality, implicit behavioral data (swaps, skips, saves) to capture true user intent and predict future engagement. • Model temporal dynamics and changing tastes. Architect sequential and recency-aware systems that adapt to shifting user preferences, ensuring recommendations reflect current intent rather than stale history. • Solve the cold-start problem. Leverage cohort signals, clustering, and content embeddings to generalize learnings across users, ensuring that even a new customer’s first box feels deeply personalized. • Bridge ML and constrained optimization. Integrate model scores (e.g., predicted conversion) with operations-research engines to perform business-aware re-ranking, balancing personalization with hard constraints like diet, budget, and inventory. • Advance the modeling. Evolve our systems using the architectures that drive modern, high-scale personalization, such as multi-stage retrieval and ranking, learning-to-rank (LTR), matrix factorization, and gradient-boosted trees. You will also evaluate and integrate more sophisticated techniques (like contextual bandits or sequence modeling) as our data complexity grows. • Drive rigorous experimentation. Define robust offline evaluation metrics (e.g., NDCG, MAP) and design online A/B tests to measure true causal impact on customer retention and satisfaction

• Own the definition, consistency, and integrity of Oura’s core business metrics across product, finance, and go-to-market. • Establish and maintain a unified “metrics layer” that ensures a single source of truth for executive and operational decision-making. • Resolve ambiguity in metric definitions and drive alignment across senior stakeholders when competing interpretations exist. • Ensure critical KPIs are consistently defined, reproducible, and trusted across the organization. • Partner with the CFO’s office and FP&A to define, document, and deliver the financial KPIs that show up in Board and exec reporting including but not limited to (Revenue, Gross profit/margin, adjusted EBITDA, Net Income, Units Sold, Warranty Rate, Membership, Engagement, and Retention and the long tail of corporate metrics). • Establish each financial KPI as a governed, audit-grade data product — locked definitions, single owner, quality tests, full lineage to source systems, formal sign-off by key stakeholders, and a documented change-management process. • Treat key data pipelines and metrics as production-grade systems with defined reliability standards. • Establish monitoring, validation, and escalation processes for data issues affecting tier-1 business metrics. • Drive structured incident management processes for data breaks, metric regressions, and reporting inconsistencies. • Improve end-to-end data quality across systems of record, transformation layers, and reporting tools. • Ensure the analytics delivered in external audit cycles — documentation, walk auditors through lineage, and ensure SOX-aligned controls and segregation-of-duties practices are reflected in how financial metrics are produced. • Coordinate large-scale initiatives spanning Data Engineering, Analytics, Finance, GTM, Product, Operations, and Technology. • Manage complex dependencies across business and technical systems to ensure consistent data flow and logic. • Establish governance forums and decision frameworks that ensure clarity, accountability, and fast resolution of tradeoffs. • Translate fragmented systems into coherent operating models that support scalable decision-making. • Design how data is used to drive planning, forecasting, and resource allocation across the company. • Partner with Finance and BizOps to ensure planning systems are grounded in consistent, trusted data. • Build mechanisms that connect operational metrics to strategic decision-making and executive prioritization. • Enable faster, higher-confidence decisions by improving signal clarity and reducing ambiguity in core business data. • Translate complex technical and systems challenges into clear, decision-oriented narratives for executives. • Provide structured updates on data health, system integrity, and transformation progress. • Drive organizational adoption of standardized metrics, reporting systems, and data definitions. • Influence without authority across senior leaders to ensure alignment on the “one version of truth.”