Statistical Programmer II

Overview

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• Independently translate analytic specifications from a statistical analysis plan into R code to create analytic datasets, generating descriptive and inferential statistics, data visualizations, often involving client variables or complex statistical methods, in consultation with the study principal investigator.
• Serve as subject matter expert on appropriate use cases for, and nuances of, the variety of different data modalities, including EHR-derived real-world data, clinico-genomics data, ML-extracted data, and claims data.
• Develop a proficient understanding of cancer biology, therapy, and/or epidemiology across multiple major tumor types and apply this understanding when crafting analytic code.
• Provide mentorship and support to more junior statistical programming staff.
• Collaborate with cross-functional stakeholders across the medical and scientific organization to execute and deliver on client-sponsored research studies in an accurate, effective, and timely manner.
• Contribute to continuous improvement of proprietary analytical tooling and templates, acting as liaison to the relevant teams and stakeholders.
• Continue to develop a deeper understanding of real-world data and related methodologies used to generate real-world evidence.
• Work closely with Epidemiology and Biostatistics to assure output quality by providing expert feedback on SAP, analytic/TLF specifications from a functional perspective.

• Doctorate degree (e.g., PhD, ScD, DrPH) in Biostatistics/Statistics, Data Science, Bioinformatics, Biological Sciences, Public Health, Math, or a closely related field with 3-4 years of relevant experience, or a Master's degree with 6-7 years of relevant experience or a bachelor's degree with 7-8 years of relevant experience.
• In addition, you're an analytical thinker and excellent communicator with experience analyzing real-world data (e.g., healthcare claims or electronic health records).
• Excellent programmer in R (including tidyverse) and proficient with Git-based environments (e.g., Github, Gitlab).
• Solid experience in creating Tables, Listings, and Graphs using R packages.
• Create/review programming documents (e.g., programming plan, specification for datasets and output template).
• Knowledge of ICH guidelines, FDA / EMA / other regulatory authority guidance from a programming standpoint.
• Extensive experience with large healthcare-related datasets (e.g., administrative claims, electronic medical records, genomics).
• Experience leading teams, either as a manager or project/team lead.
• Familiar with CDISC conventions, i.e. SDTM and ADaM models (using SAS) and related controlled terminologies, and knowledge or some experience using these models.

• Mid-Senior level

• Contract

• Information Technology

• Pharmaceutical Manufacturing