Statistics Internship
Cambridge
Job Title: Bayesian methods in casual inference.
AstraZeneca is a global, science-led biopharmaceutical business and its innovative medicines are used by millions of patients worldwide. AstraZeneca Summer Internships introduce you to the world of ground-breaking drug development, embedding you in highly dedicated teams, committed to delivering life-changing medicines to patients. Our 10–12-week program is designed for undergraduate, master's, and doctoral students. We offer exciting opportunities across Research & Development, Operations, and Enabling Units (Corporate functions).
Our internships immerse students in the pharmaceutical industry, allowing the opportunity to contribute to our diverse pipeline of medicines whether in the lab or outside of it. You will feel trusted and empowered to take on new challenges, but with all the help and guidance you need to succeed. This internship will help you develop essential skills, expand your knowledge, and build a network that will set you up for future success. You will be surrounded by curious, passionate, and open-minded professionals eager to learn and follow the science, fostering your growth in a truly collaborative and global team.
Department: Statistical Innovation.
Department description: The Statistical Innovation group provides expert statistical methodology development, training and project-specific guidance to biometrics teams across BioPharma R&D (Cardiovascular, Renal and Metabolism (CVRM), Respiratory & Immunology (R&I) and Vaccine & Immunotherapies (V&I)) and Rare Diseases. We also undertake research, developing statistical methods for the design and analysis of randomised controlled trials (RCT).
Role: The old adage "correlation is not causation" remains true, but we now have a framework which allows us to infer causal relationships between variables. In the pharmaceutical industry, establishing drug efficacy often requires us to demonstrate causality between treatment and patient improvement. Bayesian statistics enables us to quantify the uncertainty within these causal models.
Our project combines modern causal inference methods with Bayesian approaches to estimate causal population-level treatment effects and their associated uncertainty. The work involves completing and submitting our tutorial article to a peer-reviewed statistical journal. The article describes a statistical technique called “g-formula” for estimating two types of treatment effects (or marginal effects) in observational studies;
the total effect averaged over external variables (confounders) in the presence of missing data,
a decomposed treatment effect comprising a natural direct effect and indirect (or mediated effect) effect moderated by subgroups (defined by variables such as sex or weight). A literature review of frequentist and Bayesian methods for causal inference will be conducted, focusing on estimating marginal and mediated effects in the presence of confounders using techniques such as “g-formula” and propensity score methods (including inverse probability weighting and matching). A comparison of the two statistical approaches will be performed, highlighting the limitations of the frequentist approach in sparse data or where prior data is readily available. After extracting and preparing the applied dataset, we will implement R code to execute the method and document the results. Finally, after reviewing and discussing the findings with all contributors, the paper will be submitted.
Required: Have a degree in mathematics and currently undertaking or have an MSc in Statistics. Basic R or Python programming skills are essential.
Desirable: Knowledge of Bayesian statistics.
AstraZeneca is where you can immerse yourself in groundbreaking work with real patient impact.
Trusted to work on important projects, you’ll have the independence to take on new challenges while receiving all the guidance you need to succeed. Our collaborative environment is designed to help you grow professionally and personally, surrounded by passionate individuals eager to make a difference.
Our mission is to build an inclusive and equitable environment. We want people to feel they belong at AstraZeneca, starting with the recruitment process. We welcome and consider applications from all qualified candidates, regardless of characteristics.
We offer reasonable adjustments/accommodations to help all candidates to perform at their best. If you have a need for any reasonable adjustments/accommodations, please complete the section in the application form.
Ready to make an impact? Apply now and join us on this excitingjourney!
#Earlytalent
Date Posted
21-Jan-2026
Closing Date
04-Feb-2026Our mission is to build an inclusive and equitable environment. We want people to feel they belong at AstraZeneca and Alexion, starting with our recruitment process. We welcome and consider applications from all qualified candidates, regardless of characteristics. We offer reasonable adjustments/accommodations to help all candidates to perform at their best. If you have a need for any adjustments/accommodations, please complete the section in the application form.
