Sr. Principal Data Scientist – Market Access

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.
Do you want to build agentic AI systems that reach real patients and inform real business decisions?
The Lilly USA Technology at Lilly Market Access team is seeking a Senior Principal Data Scientist to design and build the next generation of agentic AI and machine learning solutions across our employer-intelligence and real-world-evidence initiatives. You will work with internal business and Technology at Lilly partners to solve complex, high-volume problems with intelligent, autonomous, and measurable AI systems that scale horizontally across use cases.
What You'll Be Doing:
As a Senior Principal Data Scientist, you will analyze complex data, develop predictive and generative models, and design agentic AI systems that solve genuine business problems in the employer and real-world-evidence spaces. You will own the AI reasoning layer end to end, from framing the problem and designing multi-agent workflows to building the models and evaluation frameworks that make these systems trustworthy. You will work closely with cross-functional teams to understand business problems, develop hypotheses, and test them with data and rigorous evaluation. You will build reusable, horizontal capabilities rather than one-off solutions.
How You'll Succeed:
Design and build agentic AI systems, including agent-to-agent orchestration, multi-agent workflows, tool and environment use, and autonomous reasoning loops that act on real systems.
Build rigorous AI evaluation frameworks, including groundedness and hallucination measurement, benchmark design, regression testing, confidence and uncertainty estimation, and human-in-the-loop review, so AI behavior is measurable and defensible.
Apply ML fundamentals, including supervised and unsupervised learning, anomaly detection, natural language processing, time-series, and statistical inference, to the employer-signal and real-world-evidence domains.
Develop, fine-tune, and distill models, including small, specialized models trained on proprietary data, with appropriate governance and data controls.
Apply modern LLM techniques, including prompt engineering, retrieval-augmented generation, embeddings, vector search, and knowledge-graph methods, to ground AI reasoning in trusted, cited sources.
Design reusable, horizontal AI capabilities that can be surfaced across multiple downstream experiences and teams, rather than rebuilding logic for each use case.
Ensure data quality, reproducibility, and auditability across all models and agentic workflows, consistent with a regulated environment.
Translate business problems into solvable AI problems and communicate insights and recommendations to both technical and non-technical stakeholders.
Conduct research and stay current with the AI frontier, bringing emerging agentic, evaluation, and model-training techniques into the team's work.
Provide technical leadership, including setting the technical direction for agentic AI work, mentoring other team members, and defining evaluation and model-development standards for the team.
Manage and prioritize multiple projects simultaneously.
What You Should Bring:
Foundational knowledge in communication, leadership, teamwork, problem solving, solution and blueprint definition, business acumen, architectural processes (e.g., blueprinting, reference architecture, governance), technical standards, project delivery, and the industry.
Business Analysis and Technical Leadership, including:
Engaging with the business and proactively seeking opportunities to deliver business value.
Understanding business requirements and effectively translating business needs and processes into technical terms, and vice versa.
Eliciting and defining requirements.
Ensuring appropriate business roles are engaged in solution execution.
Participating in design reviews to ensure traceability of requirements.
Networking with appropriate Technology at Lilly colleagues to determine solutions that meet business partners' needs.
Seeking opportunities to reuse existing processes and services to streamline support and implementation of key systems.
Staying abreast of tools and technologies to influence Technology at Lilly strategy so that it provides the best usage opportunities for the business.
Basic Qualifications:
PhD in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative or technical field, with at least 4 + years of experience developing, deploying, or maintaining AI, machine learning, or data systems in production environments.
OR Master’s degree in Computer Science, Computer Engineering, Data Science, Statistics, Mathematics, or a related quantitative or technical field, with at least 6 + years of experience developing, deploying, or maintaining AI, machine learning, or data systems in production environments.
OR Bachelor’s degree in Computer Science, Computer Engineering, Data Science, Statistics, Mathematics, or a related quantitative or technical field, with at least 8 + years of experience developing, deploying, or maintaining AI, machine learning, or data systems in production environments.
Preferred Qualifications:
A track record of delivering LLM or agentic AI solutions into production that produced measurable business results.
Strong foundation in machine learning algorithms and statistical analysis.
Proficiency in Python.
Proven experience providing technical leadership, such as setting the technical direction of AI or ML projects, mentoring data scientists or engineers, or defining standards for model development and evaluation.
Agentic AI: experience designing agent-to-agent orchestration and multi-agent workflows with modern agentic frameworks (e.g., LangGraph, LangChain, or equivalent), including tool use and autonomous reasoning loops.
AI evaluation: experience building evaluation and benchmarking approaches for LLM and agentic systems, including groundedness, hallucination, and reliability measurement, and integrating evaluation into the development lifecycle.
LLM techniques: experience with prompt engineering, retrieval-augmented generation, embeddings, vector databases, knowledge graphs, and fine-tuning or distillation of small, specialized models.
ML fundamentals: experience with supervised and unsupervised learning, anomaly detection, NLP, time-series, and uncertainty quantification.
Platforms and tooling: experience with cloud platforms such as AWS or Azure, Databricks, SQL and NoSQL data stores, deep learning frameworks, common data-manipulation libraries, Git, and CI/CD.
Domain: previous experience in healthcare, pharma, payer or employer, or real-world evidence.
Demonstrated ability to analyze large, complex data domains, to craft practical, measurable AI solutions, and to communicate with a geographically dispersed group of business and technical colleagues.
About the Organization:
Lilly IT builds and maintains capabilities with cutting-edge technologies, as the most prominent tech companies do. Lilly IT is differe

Skills
Python, Git, AWS, Azure, CI/CD, SQL, Databricks, Machine Learning, Deep Learning, NLP