Associate Director, Real World Evidence (RWE) Analytics

Sanofi•Morristown, NJ

5d•$148,500 - $214,500•Onsite

About The Position

Sanofi has pioneered the development and delivery of transformative therapies for patients affected by debilitating diseases for over 30 years. We accomplish our goals through world-class research, collaboration with the global patient community, and with the compassion and commitment of our employees. Within Specialty Care, with a focus on rare diseases, onco-immunology, and neurology, we are dedicated to making a positive impact on the lives of the patients and families we serve. Sanofi’s Specialty Care portfolio of transformative therapies, which are marketed in countries around the world, represent groundbreaking and life-saving advances in medicine. Sanofi employees benefit from the reach and resources of one of the world's largest pharmaceutical companies with a shared commitment to improving the lives of patients. The successful Associate Director, Real World Evidence (RWE) Analytics will provide programming support and Senior oversight to implementation of analyses and studies in large databases (e.g. administrative claims, electronic health records). The individual in this role will work in close collaboration with health economics and value assessment (HEVA), medical affairs, business operations & strategy (BO&S) and commercial product teams to understand research questions and translate those into actionable items for the analytics team. These responsibilities require a strong background in data sciences, including statistics and programming. Experience leading studies and analyses that use observational research design and secondary data, experience with secondary data sources such as administrative claims and electronic health records in the US and experience in the pharmaceutical industry are required. Experience guiding more junior team members through the implementation of analyses is a must. This position will report to the Rapid Analytics and Statistics Team Lead. Join the team transforming care for people with immune challenges, rare diseases, cancers, and neurological conditions. In Specialty Care, you’ll help deliver breakthrough treatments that bring hope to patients with some of the highest unmet needs.

Requirements

BA/BS + 5 years of relevant experience OR MS/PhD + 3 years of relevant experience
Academic training in the areas of mathematics, statistics/biostatistics, statistical programming, observational research, epidemiology, health economics, or a related quantitative field (preferred)
Experience programming in R, Python, and SQL
Working knowledge of statistical methods
Experience applying epidemiological methods
Experience analyzing multiple sources of secondary patient data (e.g., electronic medical records, administrative claims)
Expertise with R, Python, and/or SQL
Excellent written and verbal communication skills, collaboration and interpersonal skills
Ability to communicate technical details to non-expert audiences
Experience leading studies and analyses that use observational research design and secondary data
Experience with secondary data sources such as administrative claims and electronic health records in the US
Experience in the pharmaceutical industry
Experience guiding more junior team members through the implementation of analyses

Responsibilities

Overseeing the implementation of analyses and studies and providing Senior support and direction to Data Analysts on the RWE team.
Assess study feasibility in a variety of data sources available for analysis, including new or less familiar data.
Propose and conduct exploratory analyses that allow deep understanding of a data source at hand (e.g., ability to generate insights and visualize output that allows researchers to make decisions about study feasibility and study design).
Analyze data based on provided definitions using traditional programming or other digital applications.
Characterizing disease epidemiology, burden of disease, rates of pre-specified outcomes based on diagnostic codes and/or treatment codes using descriptive and comparative study designs.
Review relevant literature for methods, lists of codes or validated algorithms.
Create statistical analyses plans and table shells from study protocols.
Analysis of data using traditional programming or other digital applications.
Development of algorithms, such as lines of therapy for oncology.
Traditional modeling approaches, including regression and time to event analyses.
Reporting of the results in form of study tables, dashboards or slide deck.
Communicating results to stakeholders.