Senior Statistical Programmer / Data Scientist

We are seeking a rigorous and detail-oriented Statistical Programmer / Data Scientist dedicated to the statistical validation of our Digital Health Technologies (DHTs). In this role, your primary focus will be authoring comprehensive Statistical Analysis Plans (SAPs) and executing the regulatory-grade programming required for Analytical Validation (AV) and Clinical Validation (CV) within the V3 framework.

We have digital solutions for Parkinson's disease and Huntington's disease that are used in clinical trials to measure disease progression and drug efficacy.  We are working on the formal validation and regulatory endorsement of these digital biomarkers for use a endpoints in primary or secondary endpoints in Ph3 clinical trials. 

Responsibilities:

Statistical Analysis Plans (SAPs) & Validation Design

• Analytical Validation (AV) SAPs: Co-author rigorous SAPs to demonstrate that the DHT reliably and accurately measures physiological metrics compared to a gold-standard reference device (e.g., defining methodologies like Bland-Altman analysis, Lin’s Concordance Correlation Coefficient, and Limits of Agreement).

• Clinical Validation (CV) SAPs: Co-design SAPs to evaluate the DHT's ability to measure clinically meaningful concepts within a specific context of use, utilizing appropriate longitudinal, mixed-effects, or psychometric statistical models.

• Statistical Methodology: Select, justify, and document the appropriate statistical methods and success criteria required to meet FDA and EMA evidence expectations for DHT validation.

Regulatory-Grade Programming & Code Quality

• Reproducible Execution: Write clean, modular, and highly documented code in Python (or in exceptions in R) to execute the SAPs, generating the necessary derived datasets, Tables, Listings, and Figures (TLFs) for regulatory submission.

• Code Validation & SDLC: Implement rigorous software development best practices, including version control (Git), automated unit testing, and peer code reviews, ensuring zero-defect statistical outputs.

• Traceability & Compliance: Ensure all analytical pipelines, environments, and codebases comply with 21 CFR Part 11, Good Clinical Practice (GCP), and internal Standard Operating Procedures (SOPs) for software and statistical validation.

• Data Standards: Map raw and processed DHT data into regulatory-compliant data structures (e.g., CDISC SDTM/ADaM) to facilitate seamless FDA review.

Regulatory Submissions & Documentation

• Evidence Packages: Compile and structure the final statistical reports, code packages, and validation documentation required for FDA review (e.g. DDT or IND pathways).

• Audit Readiness: Maintain comprehensive documentation detailing data lineage, programming specifications, and statistical methodologies so that all validation evidence is entirely transparent and audit-ready.

• Cross-Functional Collaboration & Communication: Act as a statistical subject matter expert, effectively communicating complex statistical methodologies and validation results to cross-functional teams (Clinical, Regulatory, Engineering)

Qualifications

• Experience: 4+ years of industry experience in a pharmaceutical, medical device, or CRO environment, specifically focused on clinical trial statistics, SAP authoring, and statistical programming.

• Regulatory Exposure: Direct experience preparing statistical evidence for regulatory bodies (FDA, EMA), particularly in demonstrating the reliability and validity of clinical assessments or devices.

• Validation Statistics: Deep expertise in statistical methods used for method comparison, agreement, and reliability (e.g., convergent/divergent validity, ICC, Kappa, Bland-Altman, regression models for continuous and categorical outcomes).

• Programming Languages: Expert-level proficiency in statistical programming in Python (or in exceptions R)

• Code Quality Tools: Mastery of version control (Git/GitHub) and statistical programming validation frameworks.

• Optional - Regulatory Frameworks: Thorough understanding of the V3 framework (specifically Analytical and Clinical Validation) and the FDA’s guidance on the use of DHTs for Remote Data Acquisition in Clinical Investigations.

• Clinical Data Standards: Strong working knowledge of CDISC standards (SDTM, ADaM) (Optional: and how they apply to high-frequency or non-traditional digital health data)