Professional Summary
Principal Statistical Programmer with 12 years of experience designing and implementing scalable statistical models and analysis pipelines. Specialises in R and Python solutions that inform critical clinical decisions while maintaining regulatory compliance. Proven track record of improving analysis cycle times and mentoring cross-functional teams.
Skills
- Communication
- Project Management
- Leadership
- R
- Problem Solving
- Python
- Java
- Linux
- GIT
- SAS
- Clinical Data Standards
- CDISC
Experience
Principal Statistical Programmer — Trigent Software Inc
- Architected an R-based analytics platform that reduced statistical analysis cycle time by 47% across 12 clinical programmes.
- Implemented CI/CD pipelines for SAS and R scripts, improving validation turnaround from 10 days to 2 days and increasing release cadence.
- Mentored a team of 6 programmers on CDISC compliance, resulting in 100% first-pass acceptance on regulatory submissions.
- Designed interactive dashboards using Shiny to visualise key trial endpoints, accelerating decision-making for project leadership.
- Spearheaded migration of legacy SAS workflows to R, eliminating 75% of manual error and enabling reusable analysis artefacts.
Statistical Programmer — Cytel
- Delivered analysis datasets and TFLs for 15+ late-stage oncology trials while ensuring compliance with CDISC ADaM standards.
- Automated ADaM derivation logic in R, cutting development time by 30% and improving traceability for regulatory submissions.
- Collaborated with biostatisticians to validate Bayesian adaptive designs, providing clear documentation for health authority reviews.
- Facilitated workshops on R programming best practices, lifting overall team competency and adoption of version control.
- Optimised data cleaning pipelines using dplyr and tidyverse principles, reducing defect rates in baseline datasets.
Principal Statistical Programmer — Development
- Led programming group supporting 8 chronic disease studies, achieving submission-ready outputs on aggressive timelines.
- Established analysis standards and peer review processes that halved rework on TFL deliverables for regulatory filings.
- Advanced R as the primary analysis tool, training statisticians on its capabilities while maintaining SAS deliverables for compliance.
- Implemented automated validation checks against source data, catching discrepancies earlier in the analysis lifecycle.
- Fostered partnerships with data management to ensure timely access to clean datasets for downstream analysis.
Education
M.S. Computer Science
Georgia Institute of Technology
Certifications
- Certified Statistical Programmer (CSP)
- SAS Certified Statistical Business Analyst
Languages
English, Hindi