AI PhD Causal Machine Learning and Digital Twins Internship

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work-but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

Organization Overview

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work-but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

Functional Overview

You will join the Clinical and Development area within Lilly’s Advanced Intelligence & Research organization, where we build and deliver advanced AI and data science solutions that accelerate clinical development and improve decision-making across the drug development lifecycle. This internship sits within our work on causal machine learning and digital twins in the clinical space. These two methods work together as a feedback loop. Causal machine learning moves beyond prediction to estimate treatment effects and reason about counterfactuals, identifying which patients are most likely to benefit from an intervention and how that effect varies across a population. Digital twins, computational models of a patient, disease course, or clinical trial, then simulate how those specific patients would respond under alternative interventions, dosing strategies, or trial designs. Together, these let us ask “what would happen if?” questions in silico, exploring dosing and treatment strategies, and generating evidence to support better clinical development decisions before committing real-world resources.

As an intern, you will be assigned a scoped project with real business impact and will work alongside experienced research scientists. Key areas of work include: developing and applying causal inference methods (e.g., causal graphs, potential outcomes, propensity and weighting methods, instrumental variables, difference-in-differences, structural causal models) to estimate treatment effects; designing and building digital twins of patients, disease trajectories, or clinical trials, models that combine mechanistic, statistical, or generative approaches and can be simulated under counterfactual scenarios; validating and calibrating those digital twins against clinical and real-world data; partnering with clinical and scientific collaborators to frame questions and communicate insights; and staying current with methodological advances to justify the methods you select.

Lilly internships run for 12 continuous weeks over the summer. Each intern actively contributes to the organization, builds a comprehensive understanding of the pharmaceutical industry, and takes part in professional development and social events throughout the summer. At the conclusion of the internship, each intern presents their project highlights, findings, recommendations, and accomplishments to senior leaders and stakeholders.

As part of Lilly's commitment to innovation, interns will have the opportunity to build fluency with AI tools used across the business. We expect interns to approach these tools with curiosity, apply critical thinking to AI-assisted work, and always prioritize accuracy, confidentiality, and ethical standards in how they use them.

Basic Qualifications

• Currently enrolled in and pursuing a PhD in Statistics, Biostatistics, Computer Science, Computational Biology, Operations Research, Mathematics/Applied Math, or a closely related quantitative field.
• Foundational knowledge of causal inference and/or statistical modeling and hands-on experience programming in Python.
• Qualified applicants must be authorized to work in the United States on a full-time basis. Lilly will not provide support for or sponsor work authorization or visas for this role, including but not limited to F-1 CPT, F-1 OPT, F-1 STEM OPT, J-1, H-1B, TN, O-1, E-3, H-1B1, or L-1.

Additional Functional Job Skills & Preference

• Deep knowledge of causal inference frameworks: potential outcomes, causal graphs / DAGs, structural causal models, propensity score and weighting methods, instrumental variables, difference-in-differences, and targeted learning.
• Experience building digital twins or simulation models, for example mechanistic or statistical modeling of disease progression, agent-based simulation, dynamical systems, probabilistic or Bayesian modeling, or synthetic patient/trial data generation, including calibrating and validating such models against observed data.
• Familiarity with clinical, real-world, or observational healthcare data and the challenges of confounding, missingness, and bias.
• Strong programming skills in Python and/or R, and comfort with modern ML and statistical modeling tooling.
• Ability to translate methodological choices and insights for clinical and business stakeholders, with strong written and verbal communication.
• Prior research or internship experience applying causal or simulation methods, and a demonstrated drive to learn, innovate, and challenge yourself for the benefit of patients.
• Prior experience using AI tools (e.g., generative AI platforms, automation tools, or AI-assisted research/analytics tools) in an academic, project, or work setting.

Additional Information

This is a hands-on research and applied machine learning internship. Interns are expected to take ownership of a scoped causal machine learning and digital twin project, collaborate closely with clinical and statistical partners, and deliver a final presentation of their results to senior leaders and stakeholders.

• Lilly arranges various intern activities including sporting events, dinners, lunch and learns, volunteer activities etc. to provide opportunities for socializing, professional development, and learning more about Lilly.
• Interns will receive 1 week of paid time off during the Lilly summer shut-down (July 5th - July 9th)
• 1:1 mentoring from an experienced professional in the function
• Interns will receive a competitive salary and free parking at their work site, as well as access to Lilly’s LIFE fitness center, bike garage, and many other discounts

• If the intern's job position requires a move from another location, Lilly will provide subsidized housing

Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form (https://careers.lilly.com/us/en/workplace-accommodation) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.

Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.

Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees. Our current groups include: Africa, Middle East, Central Asia (AMECA), Black Employees at Lilly (BE@Lilly), Chinese Culture Network (CCN), EnAble, Evolve, Lilly Indian Network (LIN), Organization of Latinx at Lilly (OLA), Pride (LGBTQ+ Allies), Veterans Leadership Network (VLN) and Women’s Initiative for Leading at Lilly (WILL).

Actual compensation will depend on a candidate’s education, experience, skills, and geographic location. The anticipated wage for this position is

Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly’s compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.