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The Associate Director, Real-World Evidence (RWE) Statistics sits within Global Statistics & Data Science (GSD) and partners with cross-functional teams in Teva R&D to provide statistical strategy and technical leadership for analysis of Real-World Data (RWD) and other data sources to support lifecycle drug development. The key responsibilities include statistical input to the evidence generation planning, medical affairs and RWE research proposal reviews, study protocol and statistical analysis plan (SAP) development and execution, results interpretation, scientific presentations and publications, interactions with agencies, and RWE/statistical methodology research. In this role, your expertise will elevate data generation, inform decision-making, and ultimately improve patient outcomes.
• Lead RWE statistical input for evidence generation planning for assigned TAs/assets, collaborating closely with global and regional Medical Affairs, Health Economics, Value and Outcomes, and other R&D stakeholders.
• Actively engage in the development and review of study concepts and protocols, ensuring alignment with objectives, and appropriate sample size and statistical methods for scientific, regulatory, and market access needs.
• Lead or oversee SAP development and execution, including table, figure, and listing shells, and output review. Collaborate with programmers/analysts to ensure timely, high-quality statistical deliverables. Develop data review plans, interpret complex data, and ensure study results are scientifically robust and actionable.
• Provide in-depth statistical review for scientific publications and reports. Work closely with internal and external stakeholders to ensure appropriate statistical analysis and results are consistently applied in all scientific and regulatory documents, presentations, and publications.
• Contribute to external interactions with regulators, payers, and other agencies.
• Demonstrate excellent understanding of advanced statistical concepts. Take a leadership role in introducing innovative statistical methods (e.g., causal inference, bias and confounding control, AI/ML) into analysis plans to improve efficiency and validity of study results. Effectively explain statistical concepts to non-statisticians.
• PhD (with 4+ years of experience) or MS (with 6+ years of experience) in Biostatistics, Statistics, or a related quantitative field.
• Pharmaceutical or related industry experience required.
• Competence in RWE study design, statistical modeling, and AI/ML methods to observational data. Knowledge of methodologies for confounding control and bias minimization in observational studies highly desired.
• Expertise with multiple RWD sources (e.g., EHR, claims, registry data); familiarity with clinical trial design.
• Proficiency in programming skills in SAS, R, and/or Python; experience with cloud-based analytics platforms is a plus.
• Ability to build strong relationships with cross-functional partners to provide cutting-edge statistical solutions, drive data-driven innovation, and achieve high performance.
• Highly motivated to learn new methodologies and technologies, open-minded and adaptable, enthusiastic about innovation and making meaningful impact.
• Excellent writing and communication skills.
• Demonstrated leadership and project management abilities.
• Experience supporting HTA submissions, or regulatory interactions is preferred.
• Track record of publications or presentations in RWE methods is preferred.
• Familiarity with clinical trial data standards (ADaM/SDTM) and data privacy regulations is preferred.
We offer a competitive benefits package, including:
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