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Guidehouse is seeking an Analytics Modernization Lead to lead the modernization of analytic, surveillance, research, and reporting workflows through the adoption of modern statistical computing, automation, and open-source technologies. This individual will serve as the technical lead responsible for analytics modernization strategy, SAS-to-R/Python migration, code validation, reproducibility standards, and modernization governance across complex public health data environments.

The Analytics Modernization Lead will work closely with biostatisticians, epidemiologists, data scientists, and informaticists to ensure legacy analytic assets are modernized while preserving scientific integrity, analytical equivalence, and operational continuity. This role serves as the bridge between statistical methodology and technical implementation, enabling scalable, maintainable, and reproducible analytics platforms.

• Serve as the technical lead for analytics modernization initiatives supporting surveillance, research, program evaluation, and data science activities.

• Develop modernization strategies, migration roadmaps, coding standards, technical governance frameworks, and implementation plans for analytic transformation efforts.

• Assess legacy analytics environments, SAS programs, macros, workflows, dependencies, and reporting pipelines to identify modernization opportunities.

• Lead migration of SAS-based workflows to R, Python, and modern analytics platforms while preserving analytic intent, business rules, statistical methodologies, and output consistency.

• Design and oversee validation frameworks to confirm equivalence between legacy and modernized analytic workflows.

• Establish and maintain code review standards, regression testing approaches, automated validation processes, and reproducibility controls.

• Collaborate with biostatisticians and epidemiologists to validate migrated outputs and ensure scientific defensibility of modernized solutions.

• Develop reusable libraries, frameworks, templates, and accelerator assets that increase consistency and reduce future development effort.

• Lead adoption of modern software engineering practices, including version control, code management, documentation standards, CI/CD, and automated testing.

• Support implementation of cloud-based analytics environments and scalable data science platforms.

• Establish enterprise standards for reusable analytics, code promotion, environment management, and technical documentation.

• Mentor technical teams in R, Python, Git, testing methodologies, reproducible analytics, and modern development practices.

• Develop technical designs, migration inventories, validation reports, implementation guidance, training materials, and knowledge transfer products.

• Support proposal efforts, solution development, and thought leadership activities related to analytics modernization, statistical computing, and open-source transformation.

• Role contingent upon contract award

• Must be a U.S. Citizen or Permanent Resident. and ability to obtain and maintain a Public Trust clearance.

• Bachelor's degree in Computer Science, Data Science, Statistics, Biostatistics, Informatics, Applied Mathematics, Engineering, or related technical field. Master's degree preferred.

• EIGHT (8) or more years of professional experience supporting analytics modernization, statistical programming, data science, software development, data engineering, or analytical platform implementation.

• Experience leading modernization or migration initiatives involving SAS-based analytics, statistical programming, reporting assets, or analytical workflows to open source platforms, specifically R.

• Expertise in SAS programming, including SAS macros, PROC SQL, data step programming, statistical procedures, reporting workflows, and production analytical environments.

• Experience in both R and Python, including development of reusable packages, libraries, analytical pipelines, and automated workflows.

• Experience converting statistical programs, analytical workflows, and reporting assets from SAS to modern open-source environments.

• Experience designing and implementing validation frameworks, regression testing approaches, automated testing methodologies, and analytical quality control processes.

• Experience validating migrated outputs and documenting analytical equivalency between legacy and modernized environments.

• Strong understanding of statistical programming principles and experience collaborating effectively with biostatisticians, epidemiologists, and data scientists on analytical validation efforts.

• Experience using Git, GitHub, Azure DevOps, CI/CD pipelines, version control, code review practices, and software development lifecycle methodologies.

• Experience developing and maintaining technical documentation, code inventories, conversion playbooks, migration plans, design specifications, and validation reports.

What Would Be Nice To Have:

• Master's degree in Data Science, Computer Science, Statistics, Biostatistics, Informatics, or related discipline.

• Experience supporting analytic workflows in secure, regulated, healthcare, public health, life sciences, or government environments.

• Strong problem-solving, stakeholder engagement, and technical communication skills.

• Experience leading technical workstreams and mentoring multidisciplinary development or analytics teams.

• Experience supporting large-scale public health surveillance, healthcare analytics, biomedical research, or population health programs.

• Experience modernizing large analytics environments consisting of hundreds or thousands of SAS programs, macros, reports, and supporting assets.

• Experience developing automated code conversion, testing, validation, code quality, or reproducibility frameworks.

• Experience with Databricks, Snowflake, Azure, AWS, Palantir Foundry, or other modern analytics platforms.

• Experience implementing CI/CD, DataOps, MLOps, infrastructure-as-code, or automated deployment frameworks.

• Experience containerizing analytical workflows using Docker, Kubernetes, or similar technologies.

• Experience developing R packages, Python libraries, reusable analytics frameworks, or enterprise coding standards.

• Experience supporting modernization of surveillance systems, public health analytics platforms, or scientific computing environments.

• Experience training technical teams on open-source analytics, software engineering best practices, and modernization methodologies.

• Experience working with state public health agencies.

• Experience supporting federal public health, healthcare, or research organizations.