Principal Statistician

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Principal Statistician specializing in Real World Analytics, offering a remote contract for up to 2 years. Requires 4+ years in the pharmaceutical industry, SAS, R, SDTM, ADaM, RWD methodologies, and a PhD or MS in Biostatistics or Statistics.
🌎 - Country
United States
πŸ’± - Currency
$ USD
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πŸ’° - Day rate
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πŸ—“οΈ - Date discovered
September 27, 2025
πŸ•’ - Project duration
More than 6 months
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🏝️ - Location type
Remote
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πŸ“„ - Contract type
Unknown
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πŸ”’ - Security clearance
Unknown
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πŸ“ - Location detailed
United States
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🧠 - Skills detailed
#Programming #Datasets #Statistics #SAS #ADaM (Analysis Data Model) #ML (Machine Learning) #R
Role description
Our client, a multinational pharmaceutical company specializing in generic drugs, is seeking a Principle Statistician - Real World Analytics for contract opportunity. This is a remote position that could last up to 2 years. Position Summary: β€’ Provide statistical support in protocol development for observational studies and/or clinical trials. β€’ Author and review of statistical analysis plans, analysis dataset specifications, and TFL shells. β€’ Work with programming and other cross-functional teams in Phase-4 non-interventional study to develop CRF, validate and review the datasets and results. β€’ Conduct programming and analysis for Medical Affairs, RWE studies. β€’ Support develop abstract, poster and manuscript as deliverables. Requirements: β€’ Experience in pharmaceutical industry to provide statistical input into the study design, statistical analysis, and reporting of interventional and observational studies. β€’ 4+ yrs experience with Phase-4 study, Medical Affairs study, Real World Evidence (RWE) or HEOR study. β€’ 4+ yrs experience in statistical software, SAS and R. β€’ 4+ yrs experience with SDTM and ADaM data standards. β€’ 4+ yrs experience with Real World Data (RWD) and RWE methodologies, such as propensity score analysis, causal inference. β€’ 4+ yrs experience with advanced statistical models such as mixed effect model approaches for repeated measures, Machine Learning (ML) methods. Education: β€’ PhD or MS in Biostatistics or Statistics