Computer System Validation Analyst – R Studio

4 days ago


Montclair, Virginia, United States Katalyst HealthCares & Life Sciences Full time
Responsibilities: Lead validation planning, risk assessment, and execution for R Studio, GitLab, and Medidata Rave EDC. Develop and execute validation documentation including: Validation Plans (VP.). User Requirements Specifications (URS.). Risk Assessments (RA.). Functional and Design Specifications. Traceability Matrix (RTM.). Test Protocols (IQ/OQ/PQ or CSA-based scripts.). Validation Summary Reports (VSR.). Collaborate with cross-functional stakeholders including Clinical Data Management, Biostatistics, IT, and QA teams to gather requirements and ensure system compliance. Coordinate and document test evidence in systems such as Val Genesis, Kneat, or other e-validation platforms. Perform gap assessments, periodic reviews, and change control assessments for validated systems. Ensure compliance with CFR Part , Annex , ICH E(R), GAMP , and FDA CSA Guidance. Support audit readiness by maintaining validation documentation and providing responses to internal/external auditors. We are seeking a Computer System Validation (CSV) Engineer with strong experience validating statistical programming environments (R Studio), source code management systems (GitLab), and Clinical Data Management Systems such as Metadata Rave EDC. The ideal candidate will be responsible for ensuring GxP compliance, data integrity, and adherence to Computer Software Assurance (CSA) principles throughout the validation lifecycle. Requirements: Bachelor's degree in computer science, Life Sciences, or related field. + years of experience in Computer System Validation in a regulated GxP environment. Hands-on validation experience with, GitLab, and Medidata Rave EDC. Strong understanding of FDA regulations ( CFR Part ), data integrity, and CSA methodology. Experience with electronic validation lifecycle systems (Val Genesis, Kneat, etc.). Strong documentation, analytical, and communication skills. Knowledge of SDLC methodologies including Agile/DevOps validation frameworks. Familiarity with clinical trials data and statistical programming environments. CSA training or certification is a plus.

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