Clinical Data Scientist/ Methodologist

Sanofi

Quick summary

Work type: On-site
Location: Bridgewater, NJMorristown, NJ
Posted: 6 days ago
Closes: Sep 10, 2026
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $161k

$97k most similar roles pay here $223k

This listing doesn't post a salary. Most similar roles pay $126,800–$195,983.

Based on 240 similar postings.

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About Sanofi

Sanofi is a French global R&D-driven and AI-powered biopharmaceutical company focused on human health through specialized medicine, vaccines, and consumer healthcare.

Sanofi currently has 15 open roles on FindRole.

Listed pay typically runs $161,250–$232,916 across 7 roles with salary data.

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At a glance

TL;DR · Clinical Data Scientist/ Methodologist

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Join the Data Assessment Center of Excellence at Sanofi as a Clinical Real World Data Scientist/Methodologist, a role that bridges theoretical concepts with practical solutions in real-world data (RWD) analysis. You will design methodologies to evaluate RWD sources for insights and evidence generation, ensuring data quality and reliability through structured assessments and frameworks. Utilizing advanced epidemiological methods and statistical programming languages like SQL, Python, R, and SAS, you will work on predictive modeling, patient segmentation, and healthcare provider identification. This role requires expertise in US EHR, claims, disease registry data, and familiarity with medical coding systems such as ICD-10 and SNOMED CT. You’ll collaborate closely with cross-functional teams to ensure methodological soundness and optimal RWD usage for reliable decision-making across the drug lifecycle.

Skills

Python R SQL SAS Snowflake ICD-10 CPT SNOMED_CT LOINC RxNorm OHDSI_OMOP_CDM EHR claims_data disease_registry_data public_health_surveillance_data US_healthcare_billing_system Cursor GitHub_Copilot Power_Automate Power_App

What you'll do

Lead feasibility assessments for RWD sources to determine suitability for research objectives.
Develop data assessment frameworks to evaluate dimensions like accuracy, completeness, and integrity.
Identify limitations of RWD sources and articulate constraints on proxy endpoints and population coverage.
Design methodologically sound recommendations to minimize misuse of RWD in insights generation.
Apply advanced epidemiological methods such as propensity score matching and time-to-event analyses.
Collaborate with cross-functional teams to ensure appropriate use of RWD for reliable decision-making.

What we're looking for

Advanced degree in Epidemiology, Biostatistics, Health Informatics, or related field.
4-5 years of experience in real-world data analysis and health outcomes research.
Expertise in epidemiological study design and statistical methods like propensity score matching.
Proficiency in SQL, Python, R, and SAS for predictive modeling and data analysis.
Knowledge of medical coding systems (ICD-10, CPT) and OHDSI OMOP CDM standardized data model.
Experience with US EHR, claims data, disease registries, and public health surveillance data.
Strong communication skills and ability to work in a fast-paced environment.

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