Biostatistics Co-op: Biomarkers & Causal Inference

Moderna; Cambridge (MA); On-site; USD 28,000 - 83,000; Full time

Benefits offered by this job

Meditation classes

Commuter benefits

Paid time off

Location-specific perks

Job summary

Moderna in Cambridge, MA is seeking a motivated Biostatistics internship within our Biometrics organization. This full-time co-op runs January 18, 2027 to June 25, 2027, and requires availability for the entire duration.

You will work with experienced statisticians on biomarkers, causal inference, CoP, and surrogate endpoints, applying fit-for-purpose methods to real-world clinical data and contributing to decisions for next-generation mRNA medicines.

Qualifications

• Enrollment in a PhD program in a quantitative field.
• Training in regression, survival analysis, and causal inference.
• Experience with causal inference and ML/DL methodologies.
• Proficiency in R, SAS, or Python for analyses and simulations.

Responsibilities

• Develop and apply fit-for-purpose statistical and causal inference approaches to evaluate biomarkers as potential CoP and surrogate endpoints for clinical benefit.
• Explore causal frameworks for surrogate endpoint evaluation, including mediation analysis, controlled efficacy, principal stratification, and other emerging methodologies.
• Design and conduct simulation studies to evaluate statistical properties and robustness of proposed methods.
• Apply statistical methods to clinical trial and biomarker data to characterize relationships among treatment, immune responses, and clinical outcomes.

Skills

Biostatistics background

Regression & survival analysis

Causal inference

Machine learning / deep learning

R / SAS / Python

Education

PhD candidate in Biostatistics / Statistics / Data Science

Tools

R

SAS

Python

Job description

Moderna in Cambridge, MA is seeking a motivated Biostatistics internship within our Biometrics organization. This full-time co-op runs January 18, 2027 to June 25, 2027, and requires availability for the entire duration.

You will work with experienced statisticians on biomarkers, causal inference, CoP, and surrogate endpoints, applying fit-for-purpose methods to real-world clinical data and contributing to decisions for next-generation mRNA medicines.