RWE Programmer (SAS)

We are seeking an experienced Real World Evidence (RWE) Programmer with strong SAS, SQL, and R programming expertise to support observational research and healthcare data analytics initiatives for pharmaceutical and biotechnology clients. The ideal candidate will have hands-on experience working with large-scale healthcare claims and Electronic Health Record (EHR) databases, developing patient cohorts, and creating high-quality analysis datasets for real-world evidence studies.

• Bachelor's degree in Computer Science, Statistics, Mathematics, Biostatistics, or a related quantitative field.
• Minimum 4 years of SAS statistical programming experience.
• Strong experience with SQL and relational databases.
• Hands-on experience with R programming.
• Experience working with Real World Evidence (RWE) projects.
• Strong understanding of observational research methods and epidemiology.
• Experience creating patient cohorts and longitudinal healthcare datasets.
• Experience with advanced statistical methods, including survival analysis and regression techniques.
• Experience working with healthcare claims and EHR databases such as:
• MarketScan
• Optum
• Medicare
• Prior experience directly supporting pharmaceutical or biotechnology companies is required.
• Excellent analytical, communication, organizational, and documentation skills.

Preferred Qualifications:

• Master's degree in Epidemiology, Biostatistics, Computer Science, Statistics, or a related discipline.
• Experience working with the OMOP Common Data Model (CDM).
• Knowledge of CDISC standards.
• Experience with Python, Databricks, or other cloud-based analytics platforms.
• Pharmaceutical industry programming experience.
• Additional training or experience in epidemiological methods.
• Develop, maintain, and validate SAS programs to manipulate and analyze large healthcare datasets.
• Design and create complex analysis datasets from multiple data sources.
• Build patient cohorts and evaluate healthcare events using observational research methodologies.
• Perform data cleaning, validation, and quality checks to identify outliers and ensure data accuracy.
• Write efficient SQL queries for data extraction, transformation, and reporting.
• Collaborate with epidemiologists, biostatisticians, and cross-functional teams to support RWE studies.
• Generate reports and documentation supporting research and analytics initiatives.
• Ensure programming deliverables meet quality standards and project timelines.
• Maintain clear documentation for programming logic, data processing, and validation activities.