Machine Learning Scientist/Sr Scientist - Uncertainty Quantification & Influencer Analysis

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world.

Purpose

Lilly TuneLab is an AI-powered drug discovery platform that provides biotech companies with access to machine learning models trained on Lilly's extensive proprietary pharmaceutical research data. Through federated learning, the platform enables Lilly to build models on broad, diverse datasets from across the biotech ecosystem while preserving partner data privacy and competitive advantages. This collaborative approach accelerates drug discovery by creating continuously improving AI models that benefit both Lilly and our biotech partners.

The Machine Learning Scientist/Sr Scientist - Uncertainty Quantification & Influencer Analysis plays a dual role within the TuneLab platform, responsible for both quantifying prediction uncertainty in federated models and attributing value from partner contributions to model predictions. This position requires expertise in statistical methods, uncertainty quantification, and data valuation, combined with knowledge of drug discovery to ensure reliable predictions and fair partnership dynamics. The role is critical for building trust in model predictions and maintaining equitable, sustainable collaborations within the TuneLab ecosystem.

• Conformal Prediction Implementation: Design and deploy conformal prediction algorithms adapted for federated learning, providing rigorous prediction intervals and confidence sets that maintain validity despite data heterogeneity across partners and distribution shifts.
• Uncertainty-Driven Valuation: Develop methods that use uncertainty quantification to assess data quality and value, identifying contributions that most effectively reduce model uncertainty in critical regions of chemical/biological space.
• Contribution Attribution Systems: Implement fair attribution mechanisms (Shapley values, influence functions, leave-one-out analysis) that quantify each partner's contribution to model performance while maintaining privacy and computational efficiency in federated settings.
• Calibration Under Heterogeneity: Create robust calibration techniques that account for varying data quality, experimental protocols, and noise levels across partners, ensuring reliable uncertainty estimates for all participants.
• Value-Weighted Aggregation: Design federated aggregation schemes that weight partner contributions based on data quality, relevance, and uncertainty reduction, optimizing global model performance while maintaining fairness.
• Active Learning Strategies: Develop uncertainty-guided and value-aware active learning approaches that identify high-value experiments across the federation, maximizing information gain while respecting partner resources and priorities.
• Risk-Aware Decision Support: Translate uncertainty estimates into risk-adjusted recommendations for drug discovery decisions, helping partners understand when to trust predictions versus conduct experiments.
• Incentive Mechanism Design: Collaborate with business development to create incentive structures based on quantified contributions and uncertainty reduction, encouraging continued high-quality data sharing while preventing gaming.
• Quality Metrics Development: Establish comprehensive quality metrics that combine uncertainty quantification with data characteristics (diversity, annotation quality, experimental consistency) to assess partner contributions holistically.
• Strategic Data Recommendations: Use uncertainty and value analysis to identify optimal characteristics for future partnerships, predicting which data types, assays, or therapeutic areas would most improve federated models.

• PhD, or Masters in Statistics, Machine Learning, Operations Research, Computational Biology, Applied Mathematics, or related field from an accredited college or university
• Minimum of 2 years of experience in the biopharmaceutical industry or related fields
• Strong theoretical foundation in probability theory, statistical inference, and uncertainty quantification

• Experience with data valuation, attribution methods, or game theory
• Understanding of federated learning constraints and privacy-preserving computation
• Experience with conformal prediction and distribution-free uncertainty quantification
• Knowledge of influence functions and data shapley methods
• Expertise in ADMET prediction and understanding of experimental uncertainty
• Publications on uncertainty quantification, data valuation, or federated learning
• Understanding of pharmaceutical partnerships and consortium dynamics
• Familiarity with regulatory requirements for model validation
• Experience with Bayesian methods and probabilistic programming
• Knowledge of mechanism design and incentive alignment
• Proficiency in implementing scalable attribution algorithms
• Strong business acumen to translate technical metrics to partnership value
• Exceptional communication skills for technical and business stakeholders
• Portfolio mindset balancing rigorous uncertainty quantification with practical partnership needs

This role is based at a Lilly site in Indianapolis, South San Francisco, or Boston with up to 10% travel (attendance expected at key industry conferences). Relocation is provided.

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 Network, Black Employees at Lilly, Chinese Culture Network, Japanese International Leadership Network (JILN), Lilly India Network, Organization of Latinx at Lilly (OLA), PRIDE (LGBTQ+ Allies), Veterans Leadership Network (VLN), Women’s Initiative for Leading at Lilly (WILL), enAble (for people with disabilities). Learn more about all of our groups.

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.

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