Clinical Data Scientist/ Methodologist

About the role

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

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

• 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 PubMed, 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

• 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 (e.g., EHR, claims, registries) to understand data limitations and to verify methodological recommendations when required

About You

• Advanced degree (Master's or PhD) in Epidemiology, Biostatistics, Health Informatics, Health Economics, Pharmacoepidemiology, or a closely related quantitative discipline

• Minimum 4-5 years for Master’s degree holder or 2-4 years for Doctoral degree holder of relevant experience in real-world data, commercial analytics, real-world evidence, health outcomes research, fit-for-purpose feasibility assessment, data quality assessment or a related field within the pharmaceutical, biotech, or health technology industry

• Experience in predictive modeling using RWD to identify at risk patient populations with a publication record in peer-review journals

• Experience in patient & healthcare provider segmentation to inform Medical and Commercial strategy

• Demonstrated expertise in epidemiological study design and statistical methods such as propensity score matching, descriptive statistics, regression analysis, predictive modelling

• Strong proficiency in statistical programming languages: SQL, Python, R, and/or SAS

• Solid working knowledge of Snowflake for database querying and data extraction

• Familiarity with medical coding systems: ICD-10, CPT, SNOMED CT, LOINC, RxNorm and experience/knowledge on OHDSI OMOP CDM standardized data model for healthcare data

• Understanding of US EHR, claims, disease registry data, public health surveillance data as well as US healthcare billing system

• Experience with AI coding tools such as Cursor, GitHub Copilot, Claude, LLM

• Knowledge of automation tools such as Power Automate, Power App (an asset not required)