Director HTA Statistics & RWE Methods

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function:
Market Access

Job Sub Function:
Health Economics Market Access

Job Category:
People Leader

All Job Posting Locations:
Horsham, Pennsylvania, United States of America, Raritan, New Jersey, United States of America

Job Description:

About Innovative Medicine

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.

Learn more at https://www.jnj.com/innovative-medicine

Johnson & Johnson Innovative Medicine (JJIM) is currently recruiting for a Director HTA Statistics & RWE Methods, within the Global Commercial Strategy Organization (GCSO), to be located in Raritan, NJ, Horsham, PA or Beerse, Belgium.

This role provides strategic and technical leadership to ensure the scientific rigor of data generation initiatives across the Johnson and Johnson portfolio of innovative medicines. Responsibilities include dataset feasibility and selection, protocol development, study design, and dissemination of results for indirect treatment comparisons, post hoc trial analyses and real-world evidence (RWE).

The role spans all therapeutic areas and supports key innovative medicines across the lifecycle, from early development through launch and post-launch.

Major Duties & Responsibilities

• Maintain deep expertise in evolving methodologies, including proactiveawareness of regulatory, Payer/HTA, and guideline/formulary evidentiary requirements.
• Review, synthesize, and disseminate external case studies and precedents to shape internal strategy and analyses.
• Act as key reviewer for the Global RWE Methods Review Board, ensuring robust governance and methodological consistency across programs.
• Lead the design of advanced study designs and statistical analyses aligned with Payer/HTA expectations (e.g., indirect treatment comparisons, causal inference methods, surrogate endpoint validations, etc.) to support market access and launch strategies for key assets.
• Provide key contributions to capability building, including training for team on Payer/HTA requirements and new methodologies and also in the development of coding libraries and AI/ML advanced analytics scaling and platforms, etc.

• Required: Postgraduate degree (MSc, PhD) in Biostatistics, Epidemiology, Data Science, Health Economics, or related field
• Preferred: PhD, PharmD, or MD

• Minimum 8 years of relevant experience in pharmaceutical, biotech, or consulting environments
• Demonstrated experience in advanced statistical methodologies relevant for Payer/HTA evidence generation including indirect treatment comparisons and RWE.

• Deep expertise in clinical trial design and the strengths and limitations of secondary data sources (claims, EMR, registries, surveys)
• Advanced knowledge of statistical methodologies for market access, including indirect treatment comparisons and causal inference approaches
• Strong understanding of methodological standards, regulatory and Payer/HTA frameworks and their application to evidence generation.
• Hands-on proficiency in statistical programming (SAS, R; desirable)
• Able to organize work into clear project plans with defined milestones, deliverables, and resources