Public Health Data Analyst

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 for further details about us and careers with Metas. Job Description: Metas Solutions is seeking an experienced Public Health Data Analyst to support our client. The Public Health Data Analyst will provide scientific, epidemiologic, statistical, and advanced analytic support. In this role, you will work with 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 Data Hub data assets and analytic platforms and support analyses across cloud-based environments, including Palantir Foundry. The Public Health Data Analyst 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 scientific, epidemiologic, statistical, and data analytic support. - 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 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, Databricks, and other 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 review analytic code using Python, R, SQL, SAS, Spark, or other 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 underst nd 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: Graduate degree in data science, epidemiology, biostatistics, public health or other quantitative fields with 5+ years of related experience. Experience developing and implementing study designs, analytic plans, and methodological approaches supporting public health surveillance, research, response, and other priority activities. Experience collaborating with epidemiologists, data scientists, data managers, and engineers to address activities affecting scientific and advanced analytic activities. Demonstrated experience analyzing large, complex healthcare or public health datasets, including EHR, administrative claims, laboratory, pharmacy, hospital, survey, or surveillance data. Experience with machine learning/AI and advanced statistical modeling, including method development, implementation validation, and interpretation. Experience developing, maintaining, and reviewing analytic code and reproducible workflows using Python, R, SQL, SAS, Spark, and other approved tools, including data preparation, quality control, validation