• Desenvolver análises exploratórias, modelagens estatísticas e modelos de machine learning para apoiar decisões de negócio. • Construir, validar, monitorar e documentar modelos preditivos, prescritivos e/ou de segmentação conforme necessidade das áreas parceiras. • Traduzir problemas de negócio em hipóteses analíticas, métricas, experimentos e soluções baseadas em dados. • Preparar, tratar e integrar bases de dados estruturadas e não estruturadas, garantindo qualidade, rastreabilidade e reprodutibilidade das análises. • Comunicar resultados técnicos de forma clara para públicos de negócio, com recomendações acionáveis e mensuração de impacto. • Atuar em parceria com times de engenharia, produto, negócio e tecnologia para colocar soluções analíticas em produção quando aplicável. • Contribuir para boas práticas de versionamento, documentação, governança, ética no uso de dados e melhoria contínua dos processos analíticos.

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