Principal Statistician - Real World Analytics

Accountabilities: • Provide statistical expertise and input into the design and development of observational studies and clinical trials.

• Author and review statistical analysis plans, analysis dataset specifications, and tables, listings, and figures (TFL) shells.

• Provide statistical support for Phase 4 non-interventional and real-world evidence studies.

• Collaborate with programming and cross-functional teams to develop case report forms, validate datasets, and review analytical results.

• Conduct statistical programming and analysis for Medical Affairs and real-world evidence studies using appropriate methodologies.

• Apply advanced statistical approaches, including propensity score methods, causal inference, mixed-effects models for repeated measures, and machine learning techniques.

• Support the interpretation and communication of study findings through abstracts, posters, manuscripts, and other scientific deliverables.

• Contribute statistical expertise throughout the study lifecycle, ensuring analyses are scientifically sound, appropriately documented, and aligned with study objectives.

Requirements:

• PhD or Master's degree in Biostatistics, Statistics, or a closely related quantitative discipline.

• Pharmaceutical industry experience providing statistical input into study design, analysis, and reporting for interventional and observational studies.

• At least 4 years of experience working on Phase 4 studies, Medical Affairs studies, Real World Evidence (RWE), or Health Economics and Outcomes Research (HEOR) studies.

• At least 4 years of hands-on experience with statistical programming and analysis using SAS and R.

• At least 4 years of experience working with SDTM and ADaM data standards.

• At least 4 years of experience with Real World Data (RWD) and RWE methodologies, including propensity score analysis and causal inference.

• At least 4 years of experience applying advanced statistical models, such as mixed-effects approaches for repeated measures and machine learning methods.

• Strong understanding of clinical research, observational study methodologies, and pharmaceutical development processes.

• Strong analytical, problem-solving, communication, and cross-functional collaboration skills.

• Ability to translate complex statistical concepts and findings into clear, scientifically rigorous deliverables.

Benefits:

• Remote work opportunity.

• Opportunity to contribute to real-world evidence and Medical Affairs research within the pharmaceutical industry.

• Exposure to Phase 4, observational, and clinical research programs.

• Opportunity to apply advanced statistical methodologies, including causal inference, mixed-effects models, and machine learning.

• Collaboration with statistical programmers and multidisciplinary research teams.

• Opportunity to contribute to scientific publications, abstracts, posters, and manuscripts.

• Senior-level exposure to the full lifecycle of real-world analytics and evidence-generation projects.

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