Clinical Data Scientist/ Methodologist

Sanofi•Morristown, NJ

3d•$100,500 - $145,167•Hybrid

About The Position

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. 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.

Requirements

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.
Requires a high level of interactive communication with diverse stakeholders.
Can work with assumptions & in a fast-paced environment.
Proven teamwork and collaboration skills.

Nice To Haves

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

Responsibilities

Designing the methodology to evaluate the fitness-for-purpose of real-world data (RWD) sources for insights or evidence generation, support the development of reliable RWD Foundation and Products.
Serving as a methodological authority and RWD data domain expert, ensuring best-in-class data selection and optimal data usage to generating reliable insights or evidence to better understanding gaps in patient care and healthcare providers involved in patient care.
Ensuring data-drive decision is reliable, trustworthy, and timely.
Leading and executing feasibility assessments for RWD sources (electronic health records, administrative claims, patient registries, wearable/digital health data) to determine suitability for specific research/business objectives.
Developing and applying structured data assessment frameworks to evaluate data quality dimensions, including accuracy, completeness, validity, timeliness, longitudinally consistency, and integrity.
Assessing the availability and representativeness of patient populations within RWD sources available in Sanofi for both internal decision-making and regulatory-grade evidence generation.
Evaluating 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.
Documenting assessment outcomes in standardized feasibility reports and communicating findings clearly to cross-functional stakeholders.
Identifying and articulating limitations of RWD sources, such as proxy endpoint constraints, population coverage gaps.
Designing methodologically sound recommendations & minimize misuse of RWD, leading to unreliable insights or evidence generation.
Ensuring 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.
Applying advanced epidemiological and biostatistical methods including propensity score methods, time-to-event analyses, sensitivity analyses, and bias assessment.
Providing 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.
Providing methodology advises ensuring deliverables from RWD Foundation, RWD Science, and RWD Products are based on medical evidence/guidelines, clinically & contextually relevant.
Working closely with analysts & data scientists to ensure methodological recommendation is realistic and implementable.
Partnering 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.
Serving as the methodological point of contact for fit-for-purpose data assessment inquiries from internal stakeholders.
Collaborating 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.
Managing external data vendors and technology partners (e.g., EHR, claims, registries) to understand data limitations and to verify methodological recommendations when required.