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Job Title: Summer 2026 Intern -  Statistical Methodology

Location: Cambridge, MA

About the Job

Are you ready to shape the future of medicine? The race is on to speed up drug discovery and development to find answers for patients and their families. Your skills could be critical in helping our teams accelerate progress.

We are seeking a motivated and intellectually curious PhD-level intern in Biostatistics or a related quantitative discipline for a 12-week summer internship in 2026. This internship will focus on statistical challenges and advanced methodologies in gene therapy clinical trials, a rapidly evolving field with unique regulatory and methodological considerations.

The intern will engage in literature review, case study analysis of an FDA or EMA-approved gene therapy, and exploration of advanced statistical techniques relevant to rare disease and small population settings. This is a hands-on role offering the opportunity to contribute to internal knowledge and potentially influence future statistical strategies in regulatory trial design.

The position is ideal for a PhD student seeking exposure to real-world problems at the interface of innovation, regulatory science, and advanced methodology.

We are an innovative global healthcare company with one purpose: to chase the miracles of science to improve people’s lives. We’re also a company where you can flourish and grow your career, with countless opportunities to explore, make connections with people, and stretch the limits of what you thought was possible. Ready to get started?

Main Responsibilities:

• Conduct a structured literature review on statistical challenges and solutions in gene therapy clinical trials (e.g., small sample sizes, external control borrowing, long-term efficacy modeling).

• Perform an in-depth case study analysis of a gene therapy approved by the FDA or EMA, including trial design, statistical strategy, and regulatory feedback.

• Explore one or more advanced statistical methodologies, such as:

• • Bayesian borrowing and hierarchical models

  • External/synthetic control arm methodology

  • Survival extrapolation or longitudinal modeling

  • Propensity score or indirect comparison (e.g., MAIC/STC)

• Implement methods in R and/or SAS with support from the team.

• Participate in weekly check-ins to present findings and get feedback.

• Prepare the statistical methodology paper and present a final deliverable (slide deck + technical memo) summarizing literature review, case study insights, and methodology exploration.

About You

Basic Qualifications:

• Currently enrolled and pursuing a PhD program in Biostatistics, Statistics, or a related field

• Completion of at least 3 years of doctoral-level coursework

• Experience with and proficient in R and/or SAS programming

• Must be able to relocate to the office location and work 40hrs/week, Monday-Friday, for the full duration of the co-op/internship

• Must be permanently authorized to work in the U.S. and not require sponsorship of an employment visa (e.g., H-1B or green card) at the time of application or in the future. Students currently on CPT, OPT, or STEM OPT usually require future sponsorship for long term employment and do not meet the requirements for this program unless eligible for an alternative long-term status that does not require company sponsorship ​

Preferred Qualifications:

• Familiarity with one or more of the following areas is a plus:

  • Rare disease trials, such as:

    • Understanding of the unique challenges in rare disease clinical development, e.g., the small and heterogeneous patient populations, limited historical data, regulatory pathways (e.g., FDA/EMA accelerated approval, orphan drug designation), and innovative trial designs (adaptive designs, basket/umbrella/platform trials).

    • Experience with endpoints suited for rare conditions, natural history studies, external/historical controls, and strategies for patient recruitment and retention in ultra-rare settings is highly valued.

  • Bayesian statistics, such as:

    • Understanding how Bayesian inference is applied in gene therapy trials to address uncertainty with small sample sizes, long-term follow-up, and rare-event safety signals.

    • Knowledge of Bayesian methods for clinical trial design and analysis, including borrowing from historical/external controls, hierarchical models for small subgroups, and dynamic borrowing frameworks (e.g., power priors, commensurate priors).

    • Familiarity with Bayesian adaptive designs (dose-escalation, futility/efficacy monitoring), posterior probability–based decision rules, and predictive probability methods to support interim decision-making.

• Strong understanding of clinical trial fundamentals and statistical inference

• Strong communication skills and ability to synthesize technical content

• Self-motivated, collaborative, and intellectually curious
  

Why Choose Us?

• Bring the miracles of science to life alongside a supportive, future-focused team.​​

• Discover endless opportunities to grow your talent and drive your career, whether it’s through a promotion or lateral move, at home or internationally.​​

• Enjoy a thoughtful, well-crafted rewards package that recognizes your contribution and amplifies your impact.​

• Impactful Work: Contribute to advancing statistical methodologies that will support the development of innovative therapies in rare diseases.

• Global Leadership: Be part of Sanofi, a leading global biopharmaceutical company with a strong commitment to scientific excellence and regulatory innovation.

• Collaborative Environment: Work alongside experienced statisticians in a supportive and intellectually stimulating setting.

• Professional Development: Gain hands-on experience in regulatory science, statistical programming (R/SAS), and advanced methodologies used in real-world clinical trials.

• Mentorship & Learning: Receive personalized mentorship and weekly feedback to help you grow both technically and professionally.

• Cutting-Edge Focus: Explore high-impact statistical problems in gene therapy trials, such as Bayesian methods, external control borrowing, and long-term outcome modeling.

• Make a Difference: Help shape the future of statistical innovation in healthcare and contribute to improving the lives of patients worldwide.

Sanofi Inc. and its U.S. affiliates are Equal Opportunity and Affirmative Action employers committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status; domestic violence victim status; atypical cellular or blood trait; genetic information (including the refusal to submit to genetic testing) or any other characteristic protected by law.

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