Associate Director, Real-World Analytics Programing

This position will serve as the lead statistical programmer for assigned real-world analytics and programming studies, independently managing programming activities from study initiation through final deliverables. The individual will be responsible for translating study protocols, statistical analysis plans (SAPs), and analysis specifications into high-quality, reproducible programming solutions and study outputs.

The Real-World Analytics Programmer will partner closely with HEOR/RWE scientists, data scientists, biostatisticians, and other cross-functional stakeholders to support the successful execution of observational research studies. The role requires strong technical expertise, attention to detail, and the ability to manage multiple studies and competing priorities in a fast-paced environment.

Responsibilities:

Serves as the lead statistical programmer for assigned real-world studies and manage programming activities from study kickoff through final delivery.

Leads programming deliverables from study initiation through final report, including analysis dataset specifications, TLF shells, validation plans, programming documentation, and final study outputs.

Reviews and understand study-specific documents including protocols, SAPs, analysis specifications, and study concept documents to ensure accurate implementation of study requirements.

Translates study designs and analytic specifications into efficient, scalable, and reproducible programming solutions.

Independently develops, validates, and maintains SAS, SQL, R, Python, and other programming solutions for cohort construction, endpoint derivation, analysis dataset creation, and study deliverables.

Generates and quality-control analysis datasets, tables, listings, and figures (TLFs) in accordance with study protocols, SAPs, analysis specifications, and internal standards.

Supports a broad range of HEOR/RWE studies, including treatment pattern analyses, burden of illness studies, comparative effectiveness and safety studies, natural history studies, and external control arm analyses.

Implements and validate analytic methods specified in study protocols and SAPs, including survival analyses, propensity score approaches, confounding adjustment methods, and sensitivity analyses.