Eli Lilly
Advisor, Federated Learning Data Scientist
Eli Lilly, Lilly, Pennsylvania, United States, 15938
Overview
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 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 put people first and seek individuals determined to make life better for people around the world. Advisor Federated Learning Data Scientist
is a leadership role responsible for identifying, assessing, and implementing algorithmic solutions that leverage diverse datasets while ensuring data privacy and security for our partners. This position requires knowledge in small molecule drug development, ADME/Tox, antibody engineering, and/or genetic medicine, combined with expertise in data science and statistical analysis to develop models using federated learning. This role will advance Lillys pipeline by designing algorithms and workflows that expedite transformative therapies. This role focuses on creating sophisticated models that learn to predict multiple, related endpoints from decentralized data sources, addressing challenges of learning from heterogeneous clients who may have data for only a subset of the desired tasks. Key Responsibilities
Multi-Task Model Design:
Architect and implement advanced multi-task learning (MTL) models that leverage shared representations across tasks to improve predictive performance and data efficiency in a federated ecosystem. Handling Data Heterogeneity:
Develop algorithms to address extreme task and feature heterogeneity across clients, including personalized models, meta-learning approaches, or gradient aggregation methods robust to non-IID data. Knowledge Transfer & Regularization:
Manage the balance between shared and task-specific learning; apply regularization to prevent negative transfer and encourage positive knowledge sharing. Problem Formulation:
Define complex biological or chemical endpoints with domain experts and translate these problems into a well-posed multi-task learning framework, identifying relevant tasks and data sources. Model Validation in MTL:
Establish validation frameworks with appropriate metrics for each task and strategies to assess overall model performance and fairness across clients and tasks. Interpretability and Explainability (XAI):
Implement XAI techniques to understand and explain predictions of multi-task models and uncover relationships between endpoints to generate scientific insights. Code & Model Governance:
Write clean, reproducible code; contribute to internal libraries and ML platforms; implement version control for data, code, and models for robust research. Cross-Functional Collaboration:
Work in a collaborative, multi-disciplinary team with software engineers, MLOps, privacy experts, and domain scientists to translate research into practical solutions. Literature Review & Innovation:
Maintain awareness of federated learning, deep learning, and related fields to drive innovation and contribute to the teams research strategy. Basic Qualifications
PhD in a data science field such as Biostatistics, Statistics, Machine Learning, Computational Biology, Computational Chemistry, Physics, Applied Mathematics, or related field from an accredited college or university Minimum of 2 years of experience in the biopharmaceutical industry or related fields, with demonstrated expertise in drug discovery and early development. Additional Preferences
Experience in developing statistical and machine learning models for complex endpoints. Broad understanding of emerging scientific and technical breakthroughs. Exceptional interpersonal and communication skills with the ability to understand, empathize, and navigate complex relationships. Strong problem-solving, analytical, and project management skills; highly self-motivated and organized. Ability to connect and influence across disciplines; independent, self-starter, work without supervision. Learning Agility : Adapt to changing circumstances, learn from past experiences, and apply learnings to new situations. Portfolio Mindset : Think with a portfolio-level mentality to align program decisions with overall goals of Catalyze360. This is a site-based role in Indianapolis (preferred) or San Diego, San Francisco, or Boston with relocation provided. Lilly is committed to equal opportunity and inclusion. If accommodation is required to submit a resume, please complete the accommodation request form. This is for applicants requesting accommodation as part of the application process, and other inquiries 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 and are open to all employees. Examples include Africa, Middle East, Central Asia Network, Black Employees at Lilly, Chinese Culture Network, Japanese International Leadership Network, Lilly India Network, Organization of Latinx at Lilly (OLA), PRIDE (LGBTQ+ Allies), Veterans Leadership Network, and Womens Initiative for Leading at Lilly (WILL). Learn more about all of our groups. Actual compensation will depend on a candidates education, experience, skills, and geographic location. The anticipated wage for this position is $142,500 - $228,800. Full-time employees will be eligible for a company bonus (depending on company and individual performance). Lilly offers a comprehensive benefits program, including eligibility for a 401(k), pension, medical, dental, vision, and prescription drug benefits, flexible benefits, life insurance, time off, and well-being programs. Lilly reserves the right to amend or terminate its compensation and benefits programs. #WeAreLilly #J-18808-Ljbffr
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 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 put people first and seek individuals determined to make life better for people around the world. Advisor Federated Learning Data Scientist
is a leadership role responsible for identifying, assessing, and implementing algorithmic solutions that leverage diverse datasets while ensuring data privacy and security for our partners. This position requires knowledge in small molecule drug development, ADME/Tox, antibody engineering, and/or genetic medicine, combined with expertise in data science and statistical analysis to develop models using federated learning. This role will advance Lillys pipeline by designing algorithms and workflows that expedite transformative therapies. This role focuses on creating sophisticated models that learn to predict multiple, related endpoints from decentralized data sources, addressing challenges of learning from heterogeneous clients who may have data for only a subset of the desired tasks. Key Responsibilities
Multi-Task Model Design:
Architect and implement advanced multi-task learning (MTL) models that leverage shared representations across tasks to improve predictive performance and data efficiency in a federated ecosystem. Handling Data Heterogeneity:
Develop algorithms to address extreme task and feature heterogeneity across clients, including personalized models, meta-learning approaches, or gradient aggregation methods robust to non-IID data. Knowledge Transfer & Regularization:
Manage the balance between shared and task-specific learning; apply regularization to prevent negative transfer and encourage positive knowledge sharing. Problem Formulation:
Define complex biological or chemical endpoints with domain experts and translate these problems into a well-posed multi-task learning framework, identifying relevant tasks and data sources. Model Validation in MTL:
Establish validation frameworks with appropriate metrics for each task and strategies to assess overall model performance and fairness across clients and tasks. Interpretability and Explainability (XAI):
Implement XAI techniques to understand and explain predictions of multi-task models and uncover relationships between endpoints to generate scientific insights. Code & Model Governance:
Write clean, reproducible code; contribute to internal libraries and ML platforms; implement version control for data, code, and models for robust research. Cross-Functional Collaboration:
Work in a collaborative, multi-disciplinary team with software engineers, MLOps, privacy experts, and domain scientists to translate research into practical solutions. Literature Review & Innovation:
Maintain awareness of federated learning, deep learning, and related fields to drive innovation and contribute to the teams research strategy. Basic Qualifications
PhD in a data science field such as Biostatistics, Statistics, Machine Learning, Computational Biology, Computational Chemistry, Physics, Applied Mathematics, or related field from an accredited college or university Minimum of 2 years of experience in the biopharmaceutical industry or related fields, with demonstrated expertise in drug discovery and early development. Additional Preferences
Experience in developing statistical and machine learning models for complex endpoints. Broad understanding of emerging scientific and technical breakthroughs. Exceptional interpersonal and communication skills with the ability to understand, empathize, and navigate complex relationships. Strong problem-solving, analytical, and project management skills; highly self-motivated and organized. Ability to connect and influence across disciplines; independent, self-starter, work without supervision. Learning Agility : Adapt to changing circumstances, learn from past experiences, and apply learnings to new situations. Portfolio Mindset : Think with a portfolio-level mentality to align program decisions with overall goals of Catalyze360. This is a site-based role in Indianapolis (preferred) or San Diego, San Francisco, or Boston with relocation provided. Lilly is committed to equal opportunity and inclusion. If accommodation is required to submit a resume, please complete the accommodation request form. This is for applicants requesting accommodation as part of the application process, and other inquiries 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 and are open to all employees. Examples include Africa, Middle East, Central Asia Network, Black Employees at Lilly, Chinese Culture Network, Japanese International Leadership Network, Lilly India Network, Organization of Latinx at Lilly (OLA), PRIDE (LGBTQ+ Allies), Veterans Leadership Network, and Womens Initiative for Leading at Lilly (WILL). Learn more about all of our groups. Actual compensation will depend on a candidates education, experience, skills, and geographic location. The anticipated wage for this position is $142,500 - $228,800. Full-time employees will be eligible for a company bonus (depending on company and individual performance). Lilly offers a comprehensive benefits program, including eligibility for a 401(k), pension, medical, dental, vision, and prescription drug benefits, flexible benefits, life insurance, time off, and well-being programs. Lilly reserves the right to amend or terminate its compensation and benefits programs. #WeAreLilly #J-18808-Ljbffr