Manager Full Time

Role Summary

The RWE Statistical Programmer supports Real-World Evidence (RWE) studies by programming, processing, and analyzing Real-World Data (RWD) from sources such as claims, EHR/EMR, registries, and other observational datasets. The role focuses on building analysis-ready datasets, producing high-quality statistical outputs (TLFs), and ensuring end-to-end traceability and quality control to support publications, regulatory submissions, label expansions and internal evidence generation.

Key Responsibilities

• Program and maintain RWE study datasets from raw RWD sources (claims, EHR/EMR, registries, etc.).
• Perform data cleaning, standardization, reconciliation, and data quality checks.
• Develop cohort selection logic including inclusion/exclusion criteria, index date, baseline and follow-up periods.
• Derive analysis variables such as treatment exposure episodes, persistence/adherence (PDC/MPR), comorbidity indices (Charlson/Elixhauser), and outcomes.
• Support statistical analyses under the guidance of statisticians/epidemiologists (descriptive analyses, regression models, time-to-event analyses).
• Implement methods such as propensity score matching/weighting/stratification, subgroup and sensitivity analyses (as required).
• Generate Tables, Listings, and Figures (TLFs) for study reports, manuscripts, HTA submissions, and internal evidence packages.
• Create patient attrition flow diagrams, treatment pathways, and utilization trend summaries.
• Perform independent QC of datasets, programs, and outputs; document findings and resolutions.
• Maintain programming documentation including specifications, QC checklists, logs, and version control artifacts.
• Ensure compliance with SOPs, data privacy requirements, and audit readiness standards.
• Collaborate with cross-functional stakeholders (Biostatistics, Epidemiology, HEOR, Medical, Data Management).
• Develop reusable macros/functions to improve efficiency and standardization across studies.

Required Qualifications

• Bachelor’s or Master’s degree in Statistics, Mathematics, Computer Science, Life Sciences, or related field.
• Overall, 12 – 15 years of experience in statistical programming with 3+ years of hands-on statistical programming experience in RWE or observational studies.

Key Skills

• Strong SAS programming skills (DATA step, PROC SQL, SAS macros; PROC REPORT/MEANS/FREQ/PHREG/LOGISTIC as applicable).
• Experience handling large datasets and performing complex derivations and cohort logic.
• Good understanding of observational study design and real-world data limitations (bias, confounding, missingness).
• Familiarity with CDISC standards (SDTM/ADaM) and/or custom RWE data models.
• Knowledge of healthcare coding systems such as ICD-9/ICD-10, CPT/HCPCS, NDC, LOINC, SNOMED (as applicable).
• Good communication skills and ability to work in cross-functional teams.
• Experience in R programming (e.g., tidyverse, survival, MatchIt, tableone) is preferred.
• Hands-on experience with propensity score methods, survival analysis, and advanced regression models.
• Experience working with databases and cloud platforms (SQL, Snowflake, Databricks, AWS/Azure).
• Exposure to publication-ready output development and Regulatory/HTA submission deliverables.