Co-Op, Clinical Biomarker Biostatistics

As a Biostatistics internship, you will work alongside experienced statisticians and cross-functional scientists as an integral member of high-performing project teams. You will have the opportunity to develop, evaluate, and apply fit-for-purpose statistical methodologies to real-world clinical development questions, with a particular focus on biomarkers, causal inference, correlates of protection (CoP), and surrogate endpoints.

Here's What You'll Do

• 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, as appropriate to the scientific question.
• Design and conduct simulation studies to evaluate statistical properties, operating characteristics, and robustness of proposed methods under clinically relevant scenarios.
• Apply statistical methods to clinical trial and biomarker data to characterize relationships among treatment, immune or biological responses, and clinical outcomes.

Here's What You'll Need (Minimum Qualifications)

• Currently enrolled in a PhD program in Biostatistics, Statistics, Applied Mathematics, Data Science, or a related quantitative field.
• Graduate-level training or research experience in statistical methodology, including regression, survival analysis, and causal inference.
• Academic or research experience with causal inference and machine learning/or deep learning methodologies.
• Proficiency in R (or SAS, Python) for statistical analyses, methodology research, and simulation studies.

Here's What You'll Bring to the Table (Preferred Qualifications)

• GPA of 3.5 or higher on a 4.0 scale, or equivalent academic achievement.
• Research experience in causal inference, biomarkers, correlates of protection (CoP), surrogate endpoints, clinical trials, or related biomedical applications.
• Strong interest in developing and applying innovative statistical methodologies to accelerate the development of mRNA vaccines and therapeutics.
• Experience with simulation studies, machine learning/deep learning, or advanced statistical modeling.
• Peer-reviewed publications, conference presentations, manuscripts, or other demonstrated experience in academic research and scientific communication.
• Ability to work independently while collaborating effectively within multidisciplinary teams.
• Strong written and verbal scientific communication skills.