Sr Statistical Programmer (Oncology), Real World Evidence – FSP

Essential Functions

• Lead development of analytic datasets through raw data processing and conduct data checks / cleaning using secondary real world data sources, including claims, EHR, and lab data (e.g., PharMetrics Plus, Flatiron, Tempus)
• Lead the feasibility of real-world data sources to characterize patient population, build patient cohorts, and define and validate key variables specific to study objectives
• Conduct and QC analyses, identification of diagnosis and treatment codes and applying statistical methods to handle censor data, confounding, and missing data
• Collaborate with epidemiologists to define specifications for descriptive and complex statistics (e.g., longitudinal analysis, survival analysis, regression models) in studies using RWD for oncology research questions, including safety analyses
• Develop and QC TFLs for protocols/reports/manuscripts using RWD (e.g. claims and EHR)
• Support development of other study documents including protocols, statistical analysis plans, and study reports
• Communicate timelines, progress reports, and results to project team and key stakeholders

Qualifications

• Master's Degree in Biostatistics, Epidemiology, Data Science or related field with 5-8 years relevant experience or PhD with 3 years relevant experience
• Strong track record of analysis of RWD using claims, EMR, and lab data (Tempus, Flatiron or PharMetrics Plus is preferred)
• Experience analyzing oncology data and safety outcomes is preferred
• Demonstrated proficiency in advanced statistical programming using SAS and/or R, macros, SQL
• Prior pharmaceutical experience and advanced knowledge of observational research study design and analytic methodologies
• Excellent analytic and communication skills with attention to detail
• Able to effectively manage and prioritize multiple tasks and projects

Benefits

The potential base pay range for this role, when annualized, is $88,300.00 - $220,900.00. The actual base pay offered may vary based on a number of factors including job-related qualifications such as knowledge, skills, education, and experience; location; and/or schedule (full or part-time).

Schedule

Full-time