Senior Data Scientist

Metas Solutions excels in providing strategic consulting, program management, and data-driven analytics to federal public health agencies. Our expertise enhances health systems and delivers measurable outcomes, helping agencies achieve their mission. We collaborate with federal partners to implement agile solutions for national public health initiatives, driving impactful changes through cutting-edge programs that strengthen communities and promote well-being. See www.metassolutions.com for further details about us and careers with Metas. Job Description: Metas Solutions is seeking an experienced Senior Data Scientist to support the Centers for Disease Control and Prevention (CDC), Office of Public Health Data, Surveillance, and Technology (OPHDST). The Senior Data Scientist will provide scientific, epidemiologic, statistical, and advanced analytic support to the Actionable Data Branch (ADB) and CDC Data Hub. In this role, you will work with CDC scientists and program staff to develop study questions and analytic approaches, conduct and review analyses of large and complex healthcare and public health datasets, interpret findings, and translate data into actionable public health insights. You will serve as a subject matter expert on CDC Data Hub data assets and analytic platforms and support analyses across cloud-based environments, including Palantir Foundry/One CDC Data Platform (1CDP). The Senior Data Scientist will also contribute to AI/ML and emerging analytic activities, including exploratory modeling, data mining, feature engineering, model validation, causal inference, and evaluation of emerging methods. The position requires a combination of public health expertise, strong quantitative and programming skills, and the ability to independently lead or support complex analytic activities from study design through analysis, validation, interpretation, and dissemination. Responsibilities: - Provide senior-level scientific, epidemiologic, statistical, and data analytic support for CDC Data Hub, ADB, and OPHDST priorities. - Serve as a subject matter expert for CDC Data Hub datasets, advising scientists and program staff on data structure, content, limitations, fitness for use, appropriate analytic methods, and interpretation. - Work independently under CDC guidance to develop study questions, study designs, analytic plans, and methodological approaches supporting public health surveillance, research, response, and other priority activities. - Conduct advanced analyses of large, complex healthcare and public health datasets, including electronic health records (EHR), administrative claims, laboratory, pharmacy, hospital, and other healthcare data. - Lead or support development and dissemination of analytic reports, methodological documentation, technical briefs, presentations, white papers, and other scientific products. - Collaborate with data scientists, epidemiologists, data managers, and engineers to ensure advanced analytic and AI/ML approaches appropriately account for data quality, transformations, lineage, dependencies, and platform capabilities. - Support exploratory and applied AI/ML analyses, including data preparation, feature engineering, model development, testing, validation, performance evaluation, and interpretation. - Conduct literature reviews and applied research to inform study designs, analytic methods, and interpretation of findings. - Conduct analyses and develop reusable analytic workflows within cloud-based and enterprise platforms, including Palantir Foundry/1CDP, Databricks, and other CDC-approved analytic environments. - Use Palantir Foundry capabilities, as applicable, to explore and analyze data, develop reusable analytic workflows and data products, perform data transformation and validation, and support visualization and interpretation of results. - Develop, maintain, and revi w analytic code using Python, R, SQL, SAS, Spark, or other CDC-approved tools and languages. - Review analyses, analytic code, methods, and supporting documentation for methodological rigor, scientific validity, reproducibility, and quality. - Collaborate with data management and engineering teams to understand data transformations, data quality considerations, and dependencies affecting scientific analyses. - Promote reusable and reproducible analytic practices through documented code, quality-control procedures, shared repositories, and knowledge transfer. Qualifications: Experience providing senior-level scientific, epidemiologic, statistical, and data analytic support for public health, biomedical or epidemiologic research. Experience developing and implementing study designs, analytic plans, and methodological approaches supporting public health surveillance, research, response, and other priority activities. []{style="font-size: 12pt; font-family: arial, helvetica, sans-s "}