Director RWE Biostatistics

EPM Scientific; Philadelphia (Philadelphia County); On-site; USD 230,000 - 270,000; Full time

Job summary

A reputable pharmaceutical organization seeks a Director of Real World Biostatistics to lead statistical analyses and methodologies across drug development. This pivotal role emphasizes mentoring junior biostatisticians, overseeing complex real-world studies, and ensuring compliance with regulatory standards. Candidates should hold a Ph.D. in a relevant field with considerable experience in pharmaceutical settings and be adept at using programming languages such as R and Python. Competitive compensation package includes a base salary of $230-270,000 plus bonuses and equity.

Qualifications

• Ph.D. required with 8+ years of industry experience.
• Strong experience in drug development using RWD.
• Expertise in leading health outcomes research.

Responsibilities

• Lead end-to-end execution of real-world studies.
• Mentor junior biostatisticians and oversee projects.
• Present analyses at scientific conferences.

Skills

Pharmaceutical data analysis

Real-world evidence expertise

Advanced biostatistical techniques

Statistical programming (R, Python)

Project leadership

Communication of statistical concepts

Education

Ph.D. in Biostatistics, Statistics, Epidemiology, or a related quantitative field

Master's degree with 10+ years of experience

Tools

R

Python

Job description

We're actively recruiting for a Director Real World Biostatistics with an exceptional, well established pharma organization. The Director, Real-World Biostatistics plays a critical leadership role in shaping strategy, methodology, and execution of real-world data (RWD) analytics across the drug and vaccine development lifecycle. This position requires deep biostatistical expertise, strategic vision, and a commitment to methodological innovation to support the development and commercialization of pharmaceutical products, with primary focus on the oncology research unit (RU).

The Director will lead the design, execution, and interpretation of RWD and other observational studies using advanced biostatistical techniques—including causal inference, comparative effectiveness, clinical outcomes assessment, longitudinal and predictive modeling, and target trial emulation—while providing mentorship and scientific leadership to assigned staff.

Key Responsibilities

Biostatistical & Methodological Leadership

• Lead and oversee end-to-end execution of real-world studies, ensuring methodological rigor, robust quality control, and compliance with regulatory expectations.
• Develop and refine statistical analysis plans; conduct and oversee complex analyses; and clearly communicate results to diverse internal and external stakeholders.
• Apply fit-for-purpose non-interventional statistical methods tailored to study objectives to ensure accurate interpretation and actionable insights.

Therapeutic and Asset Expertise

• Build deep familiarity with RU assets, applying appropriate tools—including variable definitions, code lists, and data source selection—to support analytic accuracy and reproducibility.
• Provide senior-level real-world biostatistics consultation to matrix teams, leveraging hands‑on RWD expertise and authoring technical specifications when necessary.

Project Leadership & Staff Development

• Mentor and develop junior biostatisticians, fostering a collaborative, innovative, and learning-focused environment.
• Lead statistical contributions across assigned projects, managing timelines, resources, cross-functional coordination (e.g., epidemiology, HEOR, clinical), and quality oversight.

Scientific Communication & Strategic Engagement

• Present statistical analyses and methodological innovations at internal leadership forums, scientific conferences, publications, and external stakeholder meetings.
• Strengthen organizational understanding of biostatistics and reinforce the strategic value of RWD-driven insights.

Methodological Advancement & Innovation

• Stay abreast of emerging biostatistical methodologies, industry trends, and evolving analytic standards.
• Contribute to methodological research and develop new analytical approaches to elevate study design and execution.

Regulatory Strategy & Compliance

• Provide expert biostatistical support for regulatory submissions, meetings, and responses to agency queries involving RWD.
• Maintain awareness of evolving regulatory guidance on the use of RWD and ensure alignment with external expectations.
• Offer strategic statistical counsel to inform organizational decision-making and evidence-generation strategies.

Qualifications

• Ph.D. in Biostatistics, Statistics, Epidemiology, or a related quantitative field with 8+ years of industry experience, or a Master's degree with 10+ years of experience in the pharmaceutical/biotech sector—preferably within real-world evidence, epidemiology, or health outcomes.
• Demonstrated expertise in drug development, applying innovative statistical and analytical approaches to support scientific, clinical, and business objectives.
• Proven experience leading pharmacoepidemiology and/or health outcomes research using real-world data sources (e.g., electronic health records, insurance claims, registries) and implementing observational study designs aligned with biostatistical and epidemiologic principles.
• Strong programming proficiency in languages such as R and Python, with hands-on experience analyzing and interpreting observational datasets.
• Solid understanding of regulatory frameworks governing the use of RWD and clinical trial data, with experience operating within regulated environments.
• Demonstrated project leadership experience, successfully delivering results in complex, matrixed, and cross-functional settings.
• Contributions to methodological research, including authorship or co-authorship of peer-reviewed publications in real-world data analytics or related methodological areas.

Preferred Qualifications

• Advanced expertise in causal inference methodologies, including propensity score-based approaches, doubly robust estimators such as Targeted Maximum Likelihood Estimation (TMLE), instrumental variable and principal stratification methods, and techniques for handling time-varying exposures.
• Experience conducting time-to-event analyses in the context of non-randomized and observational study designs.
• Applied experience with machine learning methods, particularly as applied to clinical, epidemiologic, or real-world datasets.
• Exceptional communication and interpersonal skills, with the ability to clearly articulate complex statistical concepts to scientific, clinical, and non-technical audiences.
• Fluency in written and spoken English, with strong scientific writing and presentation capabilities.

Salary: $230-270,000 base salary + bonus + equity + additional benefits