Clinical Data Scientist

Reference no. R2853801

Job Title: Clinical Real World Data Scientist

Department: Digital RWD & Health Intelligence (Digital RWD & HI)

Location: Toronto, ON

About the Job

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 CoE is responsible for:

• Establishing and maintaining enterprise-wide standards for RWD assessment and fitness-for-purpose data evaluation through the Data Insights & Viability Engine (DIVE).

• Supporting cross-functional teams — including R&D, Business units (Vaccines, General Medicine and Specialty Care) & Digital— with expert guidance on data source selection & suitable data usage to minimize bias and confounder.

• Building and disseminating best practices, methodological frameworks, and training resources across Sanofi's RWD ecosystem

• Driving innovation in RWD methodologies, including the integration of AI tools such as Cursor, LLM, CortexAI (as available) to accelerate speed to delivery of data selection guidance, internal study design, and/or digital product development

The Clinical RWD Scientist 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 in challenging the status quo 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.

About Sanofi

We’re an R&D-driven, AI-powered biopharma company committed to improving people’s lives and delivering compelling growth. Our deep understanding of the immune system – and innovative pipeline – enables us to invent medicines and vaccines that treat and protect millions of people around the world. Together, we chase the miracles of science to improve people’s lives.

Main Responsibilities

You will be responsible for designing and executing rigorous data assessment frameworks, evaluating the fitness-for-purpose of real-world data (RWD) sources for insights or evidence generation across the enterprise, support the development of reliable RWD Foundation and Products. The Applied RWD Scientist serves 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. This role is key to ensure data-drive decision is reliable, trustworthy, and timely.

Data Assessment & Fitness-for-Purpose Evaluation

• 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 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

•