Director, Real-World Biostatistics

Role Summary

The Director, Real-World Biostatistics (RWB) leads strategy and methodology for drug/vaccine development using real-world data. This role requires deep biostatistical expertise, strategic insight, and methodological innovation to support development and commercialization in RIIRU. Locations include Philadelphia, PA; Wavre, Belgium; Stevenage and London, UK; Waltham, MA; and Durham, NC. The Director designs and analyzes RWD and observational studies using causal inference, comparative effectiveness, clinical outcomes assessment, longitudinal and predictive modelling, and target trial emulation, and mentors staff on these projects.

Responsibilities

• Lead and oversee the execution of real-world studies, ensuring methodological rigor, quality control, and regulatory adherence.
• Create and refine statistical analysis plans, conduct complex statistical analyses, and convey findings to internal and external stakeholders.
• Apply fit-for-purpose non-interventional statistical methods tailored to specific study objectives, ensuring robust data interpretation and insight generation.
• Understand RIIRU assets to apply appropriate tools (e.g., variable definitions, code lists) and data sources, and leverage hands-on RWD expertise to guide the selection and appropriate use of complex health data sets, including experience authoring technical specification documents.
• Develop in-depth knowledge on assigned assets and act as senior-level RWB consultant on matrix teams.
• Mentor junior staff, guiding and developing their capabilities through mentorship, training, and professional growth opportunities while promoting knowledge sharing, continuous learning, and an innovative and collaborative environment.
• Lead statistical efforts for assigned projects, manage timelines, resources, quality control, and coordinate across departments (e.g., epidemiology, health economics and outcomes research, clinical).
• Engage in strategic communication within the organization and with external audiences presenting statistical analyses and insights clearly and effectively at conferences, in publications, and during key stakeholder meetings, reinforcing the value of biostatistical contributions.
• Stay informed on industry trends, incorporate emerging biostatistical methods to enhance study designs and analytics, and participate in methodological research for the development of analytical techniques.
• Provide biostatistical expertise on RWD during regulatory submissions, meeting preparations, and addressing queries to ensure alignment with regulatory standards while offering statistical guidance for organizational decision-making processes.
• Stay informed with guidance documents from regulators to industry on the use of RWD for regulatory decision-making.

Qualifications

• Required: Ph.D. in Statistics, Data Science, Epidemiology or related disciplines with 8+ years (or Masters plus 10+ years) of working within the pharmaceutical/biotech industry (preferably in real-world evidence, epidemiology, or health outcomes functional areas).
• Required: Experience in working with drug development processes and strategies, utilizing innovative statistical skills to meet project and/or business objectives.
• Required: Experience leading pharmacoepidemiology and/or health outcomes analytics using RWD (e.g., electronic health record; insurance claims; registries) and applying observational study design and biostatistical principles to clinical/epidemiological research.
• Required: Experience in programming languages (e.g., R, Python) and applied experience with observational data.
• Required: Experience in working according to regulatory requirements pertaining to RWD and clinical trials.
• Required: Experience in managing projects, delivering results in matrixed environments.
• Required: Experience in methodological research with contributions to publications in real-world data analytics.
• Preferred: Experience in causal inference methodology such as propensity score-based approaches, doubly-robust estimations including target maximum likelihood estimation (TMLE), principal stratification/instrumental variable approaches, methods for time-varying exposures.
• Preferred: Experience in time-to-event analysis in the setting of non-randomized studies.
• Preferred: Experience in machine learning.