Job Overview
Work on the intersection of biostatistics, R-based analytical workflows, and platform engineering to build scalable and reproducible systems for statistical computing. Collaborate with biostatisticians, data analysts, machine learning engineers, and platform teams to ensure statistical workflows are robust, performant, and production-ready.
Responsibilities
- Develop the long-term vision and roadmap for the statistical computing platform, enabling scalable and reproducible R-based workflows
- Build and maintain R-based analytical environments for clinical and outcomes research
- Design and operate R package infrastructure, including internal packages, dependency management, and package repositories
- Build and evolve core libraries and tooling used by biostatisticians for analysis, reporting, and model validation
- Partner with biostatisticians to productionize statistical methods and pipelines
- Enable reproducible workflows through containerization, environment management, and versioning (e.g., renv, Docker)
- Integrate statistical workflows into the broader data and AI platform ecosystem
- Optimize compute, storage, and data access for large-scale clinical and real-world datasets
- Ensure systems meet standards for auditability, reproducibility, and compliance
Qualifications
- 5+ years of industry experience in software engineering, data engineering, or scientific computing
- 3+ years of hands-on experience with R programming in production or research environments
- Experience developing and maintaining R packages and shared libraries
- Experience building or supporting data platforms, scientific computing environments, or analytical infrastructure
- Experience with cloud platforms (AWS, GCP, or Azure)
- Experience with containerization and reproducible environments (Docker, Kubernetes, etc.)
- Strong proficiency in R ecosystem tools (e.g., tidyverse, renv, devtools, pak, shiny app)
- Deep understanding of package management, dependency resolution, and reproducibility
- Ability to design and build scalable systems for analytical workloads
- Strong collaboration skills and ability to work closely with biostatistics and data science teams
- Solid software engineering fundamentals (version control, testing, CI/CD)
