Senior Statistical Programmer FSP - RWD/EPI

About the role

You will contribute by providing support to the Epidemiology team through data preparation, analytic strategy advice, and stakeholder support in various epidemiology activities. This includes programming and conducting statistical analyses under the supervision of epidemiologists and statisticians, as well as supporting data standardization, visualization, and reporting for observational data.

The role involves partnering with epidemiologists to manage relationships with internal and external stakeholders, prioritizing work across multiple projects, and ensuring successful and timely project delivery through strong communication and problem-solving.

Responsibilities

• Assist in the development of study protocols and analysis plans leveraging large Real World Data (RWD) sources (Claims and/or EHR).
• Liaise with data vendors to obtain relevant data extracts for research studies consistent with study protocols.
• Create analytical databases from data extracts to facilitate data analyses.
• Conduct analyses consistent with methods set forth in study protocols and analysis plans.
• Produce tables and figures for discussions with investigators, clients, and study reports.
• Present results internally and to clients.
• Assist in the preparation of study reports and other deliverables.
• May have supervisory responsibilities in the future.

Qualifications

• Master’s degree or PhD in epidemiology, biostatistics, statistics, bioinformatics, or economics.
• 5+ years of experience conducting Real World Evidence (RWE) analytics for the pharma industry, CRO, or academic institution.
• Intermediate to expert proficiency in SQL; SAS or R proficiency is required.
• Deep expertise analyzing RWE data sources such as Optum (Clinformatics Datamart® and Market Clarity), Truveta, and UK Biobank. Experience with clinical trial and/or registry data is desirable.
• Familiarity with relational databases and a proficient understanding of claims and ancillary file layouts.
• Experience with applied statistics including regression analysis (OLS, longitudinal, logistic, Cox, GLM/GEE), survival analyses (Kaplan-Meier, cumulative incidence, accelerated failure time models), and propensity weighting.
• Excellent project management skills; ability to prioritize multiple tasks and goals to ensure timely completion.
• Confident and competent when interacting with internal and external stakeholders.
• Strong written and verbal communication skills, with the ability to summarize and present key considerations and evidence effectively.