Director, Real-World Data & Epidemiology Analytics

Location: Princeton, NJ
Work Model: Hybrid - 3x a week mandate
Compensation: $250,000-$270,000 base + 25% bonus + LTI valued at 60% of base
Visa Sponsorship: Not available
Relocation: Not provided

The Opportunity

A leading commercial-stage oncology pharmaceutical company is seeking a Director, Real-World Data & Epidemiology Analytics to join its growing Real-World Evidence organization.

This is an ideal opportunity for a highly technical statistical and epidemiology leader who has a genuine passion for working with data and using sophisticated analytical methods to answer complex scientific and business questions.

The position blends hands-on statistical expertise, observational research, real-world data analytics, and strategic leadership. The Director will lead the design and execution of RWD and epidemiology studies and translate complex findings into evidence that can inform clinical development, market access, and health-policy decisions.

Key Responsibilities

• Lead the design, execution, analysis, and reporting of observational and epidemiologic studies supporting pharmaceutical assets and indications.
• Conduct feasibility assessments and determine the most appropriate RWD sources, including claims, EHR, registries, patient-generated data, genomics, biomarkers, social determinants of health, and clinical notes.
• Apply sophisticated statistical methodologies including:
  • Survival analysis and modeling
  • Regression analysis
  • IPTW
  • MAIC
  • Causal inference
• Maintain a strong hands-on orientation toward analytics, working with SAS, R, and/or Python across real-world claims and EHR datasets.
• Develop and oversee study protocols, statistical analysis plans, table shells, analytic datasets, programming, statistical methodology, and quality-control activities.
• Partner with cross-functional evidence leaders on survival and economic modeling supporting HTA activities.
• Present study designs, interim analyses, and final results while translating technically complex findings into meaningful insights for both technical and non-technical stakeholders.
• Evaluate emerging data sources and analytical methodologies and incorporate them into evidence-generation strategies where appropriate.
• Help evaluate and scale AI-driven analytical approaches within RWD and epidemiology.
• Support the continued development of scalable, reproducible data and analytics infrastructure and standardized processes.
• Mentor RWD/Epidemiology scientists and programmers, particularly around statistical methodology, programming, and analytical problem-solving.
• Contribute to broader RWE/Epidemiology strategy and key cross-functional initiatives.

What We're Looking For

We're particularly interested in someone who brings a strong technical statistical foundation and still enjoys being close to the data.

Required Qualifications

• Master's or PhD in Biostatistics, Epidemiology, Public Health, or a related quantitative discipline.
• 10+ years of experience within real-world evidence generation and epidemiology analytics.
• Deep expertise in observational study design and advanced statistical methodologies.
• Strong understanding of causal inference, survival analysis/modeling, regression, IPTW, MAIC, and related methods.
• Hands-on programming expertise with SAS, R, and/or Python, particularly involving claims and EHR data.
• Strong understanding of RWD source evaluation and determining data fitness for specific research questions.
• Ability to translate sophisticated statistical analyses into actionable insights for cross-functional stakeholders.
• Strong scientific communication and presentation skills.
• Demonstrated research accomplishments, including a history of peer-reviewed publications.
• Ability to operate strategically while remaining comfortable getting into the technical details of the data and analysis.

Preferred Background

• Oncology experience is strongly preferred.
• Pharmaceutical or biotechnology industry experience.
• Experience supporting evidence generation across clinical development, market access, or HTA.
• Exposure to AI/ML frameworks and emerging approaches to healthcare data analytics.
• Experience mentoring statisticians, epidemiologists, programmers, or other quantitative scientists.

The Ideal Profile

This role is particularly well suited for someone who sits at the intersection of Statistics + Epidemiology + Real-World Data.

We're looking for someone who doesn't simply oversee vendors or analytical teams from a distance. The successful candidate should have the technical credibility to challenge methodology, interrogate data, understand programming and analytical outputs, and guide complex statistical decisions.

Above all, we're seeking someone who is passionate about data and excited by the opportunity to apply sophisticated quantitative methods to meaningful questions within oncology.