Clinical Data Scientist​/Methodologist

Clinical Real World Data Scientist/ Methodologist

Join the team protecting half a billion lives every year with next-gen science, mRNA innovation, and AI-driven breakthroughs. In Vaccines, you'll help advance prevention on a global scale - and shape the future of immunization.

The Data Assessment Center of Excellence (CoE) is a specialized team within Sanofi's Digital RWD & HI function, operating at the intersection of epidemiology, RWD, data products and insights/evidence generation. The vision of the CoE is to ensure all Sanofians has the right data, used the right way, for real patient impact.

The Clinical RWD Scientist/ Methodologist is a critical role within the Data Assessment Center of Excellence (CoE), embedded in Sanofi's Digital RWD & HI function. This role bridges the gap between theoretical concepts to practical & reliable RWD solutions. You are an agile professional interested with deep subject matter expertise in US RWD, pharmaco-epidemiological methods and a quick learner of new data, methodology, and technology.

You are a proactive team member that values cross-learning, see challenges as opportunity and can work with assumptions.

Join the digital engine driving Sanofi's transformation - where AI, automation, and bold experimentation power faster science and smarter decisions. Here, you'll help build the first biopharma company powered by AI at scale.

Main Responsibilities

• Lead and execute feasibility assessments for RWD sources (electronic health records, administrative claims, patient registries, wearable/digital health data) to determine suitability for specific research/business objectives
• Develop and apply structured data assessment frameworks to evaluate data quality dimensions, including accuracy, completeness, validity, timeliness, longitudinally consistency, and integrity
• Assess the availability and representativeness of patient populations within RWD sources available in Sanofi for both internal decision-making and regulatory-grade evidence generation
• Evaluate the feasibility of extracting structured and unstructured data elements (e.g., clinical scores, patient-reported outcomes) from EHR systems, including NLP-based extraction from clinical notes
• Document assessment outcomes in standardized feasibility reports and communicate findings clearly to cross-functional stakeholders
• Identify and articulate limitations of RWD sources, such as proxy endpoint constraints, population coverage gaps

Methodological Design & Optimal RWD Usage

• Design methodologically sound recommendations & minimize misuse of RWD, leading to unreliable insights or evidence generation
• Ensure appropriate use of ICD codes, procedure codes, and other medical coding standards (sourced from peer-reviewed references such as Pub Med, Embase, and Orphanet, etc.) for patient identification, healthcare provider segmentation, clinical site identification, and phenotyping
• Apply advanced epidemiological and biostatistical methods including propensity score methods, time-to-event analyses, sensitivity analyses, and bias assessment
• Provide methodological input on the use of clinical score proxies and surrogate endpoints in RWD contexts, clearly delineating their applicability for internal versus regulatory/publication use
• Provide methodology advises ensuring deliverables from RWD Foundation, RWD Science, and RWD Products are based on medical evidence/guidelines, clinically & contextually relevant
• Work closely with analysts & data scientists to ensure methodological recommendation is realistic and implementable

Cross-Functional Collaboration & Stakeholder Engagement

• Partner with R&D, Business units (Vaccines, General Medicine and Specialty Care) & Digital teams on data identification and appropriate usage of RWD for insights / evidence generation across drug lifecycle
• Serve as the methodological point of contact for fit-for-purpose data assessment inquiries from internal stakeholders
• Collaborate with RWD Foundation, RWD Product Owners, RWD Data Sciences to ensure RWD are used appropriately to inform reliable decision making & to provide knowledge transfer on data domain expertise
• Manage external data vendors and technology partners…