Principal Statistical Programmer in R/Python

We’re seeking an experienced Statistical Programmer to lead end-to-end programming using R/Python with deep CDISC SDTM/ADaM expertise. Key Responsibilities Develop, validate, and maintain analysis datasets (ADaM) and SDTM mappings compliant with CDISC standards and sponsor conventions. Build robust, reproducible R/Python pipelines for TFLs (tables, figures, listings) and exploratory analyses; automate QC and reporting. Author and review specifications (SDTM/ADaM define.xml metadata, ADRGs, SDRGs, programming specs). Implement end-to-end workflow orchestration, version control (Git), CI/CD, and package management for regulated environments. Perform statistical programming for inferential analyses per SAP; implement estimands where applicable. Conduct code reviews, dual programming, and rigorous validation per SOPs and 21 CFR Part 11 expectations. Support eSubmission readiness (e.g., define.xml, reviewer’s guides, data conformance checks with Pinnacle 21-like workflows). Mentor programmers, contribute to libraries/utilities, and improve standard programming practices. Interface with Biostats to translate SAP into executable code; troubleshoot data issues across EDC to SDTM/ADaM flow. Collaborate on data visualization and dashboards for study teams and interim looks. Required Qualifications 5–8+ years in clinical/biopharma statistical programming with direct ownership of SDTM and ADaM deliverables for Phase I–III studies. Advanced proficiency in R and/or Python for clinical reporting: R: dplyr/tidyr, data.table, haven, broom, ggplot2, quarto/rmarkdown, pkg development. Python: pandas, numpy, statsmodels/scikit-learn (as needed), plotly/matplotlib, pyreadstat. Deep working knowledge of CDISC standards (SDTM, ADaM, Controlled Terminology), define.xml, ADRG/SDRG. Practical SAS awareness for reading/writing XPT, integrating legacy code, and interpreting SAS-based specs/logics. Strong Git, code review, and documentation discipline; experience with reproducible pipelines (e.g., make/targets, renv/packrat, virtualenv/poetry, containers). Excellent communication; ability to partner with Biostatistics and lead programming workstreams. Education BS/MS in Statistics, Biostatistics, Computer Science, Data Science, or related field. Application Question(s): How many years of experience in R programming? Work Location: Remote