Clinical Data Scientist in San Diego

Data Transformation & Delivery

• Convert raw, manually abstracted, and AI-processed clinical data into standardized formats (e.g., CDISC SDTM/ADaM) or client-specific data models.
• Build analysis-ready datasets from diverse sources including EMRs, EDC systems, and internal abstraction tools.

Statistical Programming & Reporting

• Develop and generate Tables, Listings, and Figures (TLFs) for clinical study reports and interim analyses using SAS, R, or Python.

Data Integrity & Quality Control

• Perform comprehensive data cleaning and validation checks.
• Investigate discrepancies to distinguish between true clinical complexity and upstream data errors.

Cross-Team Collaboration

• Partner with engineering teams to automate data cleaning and validation workflows.
• Serve as an early tester and internal customer for new data infrastructure tools.

Documentation

• Create and maintain clear documentation including data specifications, derivation logic, reviewers' guides, and Define.xml to support audits and regulatory submissions.

Ad-Hoc & Exploratory Analysis

• Support internal and external stakeholders with data queries, one-off analyses, and insights that demonstrate the value of the platform.

Ethical & Compliant Data Management

• Follow all applicable privacy, security, and compliance requirements (e.g., HIPAA).
• Promote ethical handling of sensitive clinical data.

The ideal candidate is detail-oriented, technically versatile, and passionate about delivering high-quality clinical data. You will work closely with cross-functional teams-including Clinical Operations, Data Platform Engineering, and AI/ML teams-to ensure accuracy, traceability, and compliance across all data outputs., * BSc or MSc in Statistics, Mathematics, Computer Science, Life Sciences, or a related field.

Experience

• 2-5+ years in clinical data science, statistical programming, or clinical data management within Pharma, Biotech, or related environment.

Technical Skills

• Strong proficiency in SAS, R, Python, and SQL.
• Familiarity with version control (Git) .
• Experience with CDISC SDTM/ADaM standards.
• Understanding of oncology endpoints (e.g., RECIST, survival analysis) and real-world data (RWD) is a plus.

Core Competencies

• Demonstrated expertise in cleaning and stitching complex datasets.
• Ability to work with unstructured text or NLP-derived outputs is highly desirable.
• Exceptional attention to detail and accuracy.
• Strong written and verbal communication skills.
• Self-directed, curious, humble, and collaborative.

• Compensation: Competitive salary range based on experience, qualifications, and location.
• Eligible for equity, annual performance bonus, and comprehensive benefits.

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