2027 Summer Intern Biostatistics Interactive Benchmarking Platform Cambridge MA

Job Title: 2027 Summer Intern Biostatistics Interactive Benchmarking Platform for Placebo Response Prediction in AD Clinical Trials

Grade: Intern

Location: Cambridge, MA

Duration: 10-12 weeks

About the Job

Looking to launch your career at the cutting edge of healthcare? Join Sanofi for a chance to develop with mentoring and guidance from inspirational leaders while helping to make an impact on the lives of countless people worldwide. We are seeking a highly motivated Summer Intern to join our team.

Ready to grow your skills while helping shape the future of healthcare? At Sanofi, you’ll be empowered to learn, ask questions, and bring your ideas to life – all while supported by inspiring mentors and collaborative teams.

About Sanofi

We’re an R&D-driven, AI-powered biopharma company committed to improving people’s lives and delivering compelling growth. Our deep understanding of the immune system – and innovative pipeline – enables us to invent medicines and vaccines that treat and protect millions of people around the world. Together, we chase the miracles of science to improve people’s lives.

Main Responsibilities

Develop an interactive Shiny app that predicts placebo response rates and variability for future Phase 2/3 AD clinical trials based on population and design characteristics, enabling proactive trial design optimization.

This internship focuses on developing an interactive benchmarking platform to predict placebo responses in Atopic Dermatitis (AD) clinical trials based on planned study population characteristics. The intern will leverage the AD Placebo iDB (internal database) to build a predictive modeling framework and deploy it as a user-friendly Shiny application.

This project focuses on AD clinical trials, where Placebo response is variable and influenced by multiple factors. The AD Placebo iDB (internal database) contains rich historical data across diverse populations and study designs. Accurate placebo prediction is critical for sample size planning, and go/no-go decisions. An interactive tool would improve efficiency and facilitate access to historical insights across clinical and statistical teams.

After exploring the data, the intern will develop a predictive modeling for placebo response. The intern will develop, document, validate and deploy an interactive platform to predict placebo response for different patient populations.

About you

 Basic Qualifications:

Currently pursuing a master’s or PhD degree in Biostatistics, Statistics, Computer Science or related field

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 Internal requirements for this program unless eligible for an alternative long-term status that does not require company sponsorship.

Must be enrolled in an accredited college or university throughout the duration of the co-op/internship. 

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

Preferred Qualifications

Machine learning experience

Data visualization (interactive plots)

Version control (Git/GitHub)

Experience with clinical trial data structures (CDISC)

Why Choose Us

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

Stretch your skills through apprenticeships, internships, graduate programs, and international experiences that grow with you.​

Learn from inspiring mentors and collaborative teams while contributing to real projects that shape the future of healthcare.​

Build your future with access to the latest tools, digital innovation, and continuous learning that keep you ahead of the curve.​​

Compensation

The salary range for this position is  $45-57 USD hourly. All compensation will be determined commensurate with demonstrated experience.

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