Senior Programmer, Clinical Data Evaluation

CSL•Waltham, MA

1d•Hybrid

About The Position

The Senior Programmer for Data Validation supports cross-functional teams such as Statistics, Data Management and Standards by overseeing and ensuring the quality of clinical data build and programming activities across in-house, hybrid, and vendor-supported studies. The role focuses on governance, review, and validation of deliverables to ensure alignment with CSL standards, regulatory requirements, and industry best practices. The incumbent applies strong expertise in CDASH and SDTM mapping, eCRF development, and EDC systems (e.g., Medidata Rave, Veeva Vault) to validate datasets, specifications, and vendor artifacts. The role will contribute to shaping future-state data capabilities by supporting initiatives around scalable data platforms and AI-driven automation, enabling more efficient and intelligent use of clinical data.

Requirements

BSc in Computer Science, Mathematics, Statistics or related area with relevant experience
5+ years of relevant experience (clinical data management, clinical programming, or a related function within a pharmaceutical or CRO environment, with exposure to in-house, hybrid, and/or vendor-supported study models)
Strong understanding of clinical data processes, including data standards, data flows, and integration of eCRF and external data sources, data quality oversight, validation processes, and vendor deliverable review
Solid knowledge of CDISC standards, including CDASH, SDTM, and ADaM, with practical experience in data mapping, validation, and submission readiness
Familiarity with EDC systems (e.g., Medidata Rave, Veeva Vault) and understanding of eCRF design and clinical data collection frameworks
Demonstrated ability to manage priorities, meet timelines, and operate effectively in a fast-paced and evolving environment
Good communication and analytical skills
Good planning and organizational skills
Ability to work successfully in a matrix organizational structure
Networking skills and ability to share knowledge and experience amongst colleagues
Fluent in English, oral and in writing

Nice To Haves

Proficiency in SAS and familiarity with Python, R, or C# is a plus to support automation and advanced analytics

Responsibilities

Collaborate with clinical study teams, Standards, and cross-functional stakeholders to define, implement, and continuously enhance data quality methods, acceptance criteria, and validation rules for eCRF and external data sources across all study models
Oversee the implementation and maintenance of data quality control frameworks, including validation check libraries and acceptance criteria for vendor and internally generated data, ensuring alignment with CSL standards and regulatory expectations
Govern and maintain a standardized library of reports and listings to support study teams, ensuring consistency, reusability, and efficiency across in-house, hybrid, and vendor-supported studies
Oversee the setup, automation, and monitoring of recurring data quality and operational reports across different models (in-house vs outsourced etc.)a ensuring accuracy, completeness, and timely availability of outputs
Partner with Data Management, Statistics, and vendors to review and validate clinical data deliverables, ensuring submission readiness in accordance with regulatory standards (e.g., SDTM, ADaM, define.xml)
Lead the development and oversight of data visualization and reporting solutions that provide insights into data quality, study progress, and key metrics to support informed decision-making
Ensure appropriate data structures, storage, and integration approaches that enable pooled analyses, traceability, and efficient retrieval of both current and legacy clinical data
Serve as a subject matter expert in CDASH, SDTM mapping, and EDC systems, providing guidance on data standards, eCRF design, and validation approaches to internal teams and external partners
Support the integration and governance of external data sources (e.g., IxRS, eDiaries, labs, ECG), ensuring data consistency, quality, and alignment with study requirements, while facilitating collaboration between internal teams and vendors
Contribute to innovation and future-state initiatives by supporting the development of scalable data platforms, automation strategies, and AI-enabled capabilities to enhance data processing, validation, and analytics