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About Eli Lilly and Company
Eli Lilly and Company is a global pharmaceutical company that discovers, develops, and markets medicines in areas such as diabetes, oncology, neuroscience, and immunology, known for products like insulin and Mounjaro. Industry: Pharmaceuticals
Eli Lilly and Company currently has
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JOB TITLE: AI PhD Causal Machine Learning and Digital Twins Internship
The AI PhD Causal Machine Learning and Digital Twins Internship is based within the Advanced Intelligence & Research organization's Clinical and Development area. In this role, you will work alongside experienced research scientists to develop and apply causal inference methods and build digital twins of patients, disease trajectories, or clinical trials. You will use these tools to estimate treatment effects, simulate counterfactual scenarios, and explore dosing strategies in silico to improve clinical development decisions. Key technical requirements include proficiency in Python and R, along with knowledge of causal graphs, potential outcomes, structural causal models, and propensity weighting. You will also utilize mechanistic, statistical, or generative modeling approaches to validate models against clinical and real-world healthcare data while addressing challenges like confounding and bias.
Develop and apply causal inference methods to estimate treatment effects across patient populations.
Design and build digital twins of patients, disease trajectories, or clinical trials using mechanistic or statistical approaches.
Validate and calibrate digital twin models against clinical and real-world data.
Partner with clinical and scientific collaborators to frame research questions and communicate technical insights.
Stay current with methodological advances to justify specific modeling and statistical choices.
Take ownership of a scoped project and deliver a final presentation of findings to senior leaders.
Use AI tools to assist with research and analytics while maintaining accuracy and ethical standards.
What we're looking for
Currently enrolled in and pursuing a PhD in Statistics, Biostatistics, Computer Science, Computational Biology, Operations Research, Mathematics/Applied Math, or a related quantitative field.
Possess foundational knowledge of causal inference and/or statistical modeling.
Have hands-on experience programming in Python.
Must be authorized to work in the United States on a full-time basis.
Deep knowledge of causal inference frameworks including potential outcomes, DAGs, structural causal models, and instrumental variables (preferred).
Experience building digital twins or simulation models for disease progression or clinical trials (preferred).
Familiarity with clinical, real-world, or observational healthcare data and challenges like confounding and bias (preferred).
Strong programming skills in R and ability to communicate insights to clinical and business stakeholders (preferred).
Machine Learning
Large Language Models
Diffusion Models
Reinforcement Learning
Python
Swift
Objective C
Java
TensorFlow
PyTorch
CoreFlow
Linear Algebra
Statistics
Time Series Analysis
Optimization
Causal Inference
Stochastic Modeling
Python
Swift
Objective C
Java
TensorFlow
PyTorch
CoreFlow
Machine Learning
Large Language Models
Diffusion Models
Reinforcement Learning
Linear Algebra
Statistics
Time Series Analysis
Stochastic Modeling
Optimization
Causal Inference
Machine Learning
Artificial Intelligence
Python
Java
C++
TensorFlow
PyTorch
MLFlow
Natural Language Processing
Neural Networks
Graph Representation Learning
Big Data Analytics
Data Engineering
Recommender Systems
AI-native Engineering
Inference
Training