We are looking for an Associate Director of Real-World Evidence, Pharmacoepidemiology to support projects at the NIH and to lead and inspire our RWE team. The successful applicant will be involved in designing, leading, and conducting RWE studies investigating drug safety and effectiveness and other observational studies in support of the National Clinical Cohort Collaborative (N3C), the All of Us Research Program, and other public health initiatives. The successful candidate will demonstrate exceptional leadership, team management, and strategic vision to drive the team's success.

The ideal candidate is a seasoned leader with a strong background in pharmacoepidemiology and real-world evidence generation. This individual excels in managing and inspiring diverse teams, fostering a collaborative and high-performance culture, and aligning team goals with organizational objectives. They possess deep expertise in designing and interpreting real-world evidence studies using advanced statistical methods and various data sources, coupled with a thorough understanding of regulatory guidelines. They will have experience in the application of causal inference methods (IPTW, PSM, G-methods, etc…) and observation study designs (target trial emulation, self-controlled case series, test-negative design, etc…). The ideal candidate is a strategic thinker and problem solver, adept at leveraging innovative approaches and new data technologies. They can oversee RWE projects start to finish and several first author or corresponding author publications in the field. They have excellent communication skills, enabling effective collaboration with cross-functional teams and key external stakeholders. The candidate will have a deep understanding of the limitations of real-world data and various forms of bias and confounding that arise in observation studies (immortal time bias, confounding due to indication, etc…). They will work with clinicians, biostatisticians, and other stakeholders to identify research questions of significant public health importance. They will stay current with scientific and technical literature; stay informed about new approaches and methodologies, including participation in conferences, and/or workshops, publishing scientific papers, and independently locate and utilize scientific resources.

Responsibilities

• Lead and manage a team of epidemiologists, data scientists, and analysts in the design, execution, and interpretation of real-world evidence studies, including retrospective and prospective observational studies and registries.

• Develop and implement a strategic vision for the RWE team, ensuring alignment with organizational goals and business objectives.

• Serve as an RWE subject matter expert (SME) to NCATS leadership.

• Help develop standards for the operationalization and fit-for-purpose use of AI/ML methods applied to Real-World Data (RWD).

• Produce exemplary material, including tools geared toward under-served communities and new learners, covering topics relevant to use of RWD including but not limited to treatment of missing data, case control selection, minimization of type I/II error and bias, prevention of model overfitting and poor generalization and approaches to Electronic Health Record (EHR) data limitations.

• Manage, schedule, and oversee outreach initiatives to underserved communities and extramural sites with limited bioinformatics capacities.

• Support investigations from the Office of the Director and N3C leadership requiring biostatistical and/or machine learning knowledge and domain expertise. Outputs of these may result in publications.

• Produce criteria to evaluate the incremental benefit of PPRL-linked datasets (i.e., improvements in the quality and/or scope of analyses of N3C data, reduction in data missingness, etc.).

• Participate in N3C Enclave Knowledge Store object and code template peer review, integration and unit testing, and validation.

Qualifications

The ideal candidate will have:

• Doctoral degree (PhD, DrPH, ScD) in Epidemiology, Pharmacoepidemiology, Public Health, or a related field. A Master's degree with significant relevant experience will also be considered.

• At least 8 years of experience in pharmacoepidemiology, real-world evidence generation, or related fields within the pharmaceutical or biotech industry.

• Proven track record of working with real world clinical data via first or corresponding author publications.

• Deep understanding of the fundamentals of biostatistics (survival methods, bias, confounding, etc…)

• Demonstrable experience in causal inference methods.

• Fluent in Python, R, and SQL.

• Familiarity with machine learning and artificial intelligence techniques applied to RWD.

• Demonstrated leadership and team management skills, with experience leading and mentoring high-performing teams.

• Strong oral and written communication skills and ability to actively participate in technical discussions and follow up on action items.

• Sound judgment, positive attitude, and creative outlook required.

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