Overview:
As the Clinical Data Scientist - RWE Data Analysis Oversight Lead for Inflammation, you will oversee the timely, high-quality analysis deliverables across the TA, partnering with scientific leads and vendors. You will shape data strategy and analytical standards, ensure compliant programming, and manage cross-program priorities. You’ll act as the main point of contact for vendors, driving risk mitigation and end-to-end delivery. This role offers impact at a portfolio level within a biologics-focused company. Compensation / Benefits salary range specified discretionary annual bonus discretionary stock-based long-term incentives paid time off medical, dental, vision, life insurance benefits package information available
Responsibilities:
Track and prioritize analysis deliverables across the TA portfolio with the CDS-RWE TA Head Partner on data strategy and advanced analytical tools to enable portfolio priorities Oversee vendor performance and risk escalation for programming and analyses Manage vendor oversight plans, quality checks, and deliverables per oversight process Own end-to-end oversight of analyses ensuring timely, high-quality outputs aligned to TA priorities Coordinate with scientific leads to translate observational research protocols into deliverables Review programming outputs for quality and completeness (programs, cohorts, results, publications) Ensure proper locking/archiving of deliverables and coordinate analysis assignments to vendors Escalate quality or scope issues with vendors and service providers Maintain regular vendor communications to meet milestones and priorities Oversee onboarding of vendors to data analysis tools and environments Identify opportunities for efficiency improvements and standardization Collaborate with other TAs for uniformity and with the Gilead Data Office on datasets awareness/training
Key requirements:
Bachelor's Degree or higher with significant experience (minimum 8–12 years depending on degree) 12+ years' experience for Bachelor's level; 10 years for Master’s; 8 years for PhD as per description Hands-on experience with primary and real-world data sources in GxP environments Proven leadership in managing programmers/data analysts in a matrixed setting Strong ability to review programs and outputs for quality and best practices Clear communication of scope, requirements, and standards; ability to interpret results Strong problem-solving with data-driven approaches Cross-functional collaboration with programs, studies, and vendors Understand RWE regulatory landscape; experience with international datasets and/or OMOP preferred strong written and verbal communication collaboration across functions problem-solving mindset SAS R GxP-compliant systems
