Data Scientist at Eli Lilly and Company in US, Indianapolis IN

Summary: Eli Lilly and Company is a global healthcare leader headquartered in Indianapolis, Indiana, dedicated to improving lives through innovative medicines. They are seeking a Data Scientist to support Global Regulatory Affairs, Global Scientific Communications, and Global Statistical Sciences by applying data science, machine learning, and AI techniques to solve business problems and enhance decision-making processes. Responsibilities: - Partnering with business stakeholders across GRA, GSC, and GSS to understand their workflows, identify high-impact problems, and frame them as data science opportunities — translating business questions into analytical approaches - Developing and deploying machine learning models — classification, regression, clustering, time-series forecasting — to solve problems such as submission timeline prediction, document classification, regulatory risk scoring, and resource optimization - Building and evaluating NLP and generative AI solutions — leveraging LLMs, RAG architectures, text extraction, entity recognition, and document summarization to automate regulatory authoring, scientific literature analysis, and content generation workflows - Designing and executing experiments to evaluate model performance — using rigorous statistical methods, A/B testing, and evaluation frameworks (including RAGAS for RAG systems) to ensure solutions meet quality and accuracy thresholds before deployment - Designing and building AI agents and agentic workflows — creating multi-step, tool-using systems that can autonomously execute complex tasks such as regulatory document drafting, data extraction and transformation, and cross-system orchestration — moving beyond single-prompt interactions to production-grade agent architectures that operate reliably in a validated environment - Collaborating with full stack engineers and platform teams to productionize models — building APIs, integrating into existing applications, deploying on AWS infrastructure (Lambda, EKS, SageMaker, Databricks), and monitoring model performance in production - Communicating findings and recommendations to both technical and non-technical audiences — using data visualization, storytelling, and clear business-impact framing to ensure your work drives actual decisions - Staying current with emerging techniques in machine learning, generative AI, and data science — evaluating new tools, frameworks, and approaches for applicability to the GRA/GSC/GSS portfolio and sharing knowledge with the broader team Required Qualifications: - Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field - 1 years of professional data science experience in Python, R and core data science libraries Preferred Qualifications: - Experience with machine learning frameworks and model deployment patterns - Academic Background in Data Science - Hands-on experience with NLP techniques and/or generative AI — LLM APIs (OpenAI, Anthropic), RAG architectures, vector databases, prompt engineering - Familiarity with cloud data platforms — AWS (SageMaker, Lambda, S3), Databricks, or similar - Knowledge of statistical methods — hypothesis testing, experimental design, Bayesian methods, regression analysis - Experience with SAS programming - Strong communication skills — ability to present technical findings to non-technical audiences and translate business questions into analytical frameworks - Collaborative mindset and experience working with cross-functional teams including engineers, product owners, and business partners Required Skills: Machine Learning, Data Science, Natural Language Processing, Python Important Skills: AWS, Statistical Methods, Generative AI Nice-to-Have Skills: Collaborative Mindset Benefits: 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, Well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities)