Analytics Modernization Lead

Analytics Modernization Lead

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, Git Hub, Azure Dev Ops, 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.

• Master's degree in Data Science, Computer…