Advisor, Data Scientist - CMC Data Products

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. 

Organizational & Position Overview:

The Bioproduct Research & Development (BR&D) organization strives to deliver creative medicines to patients by developing and commercializing insulins, monoclonal antibodies, novel therapeutic proteins, peptides, oligonucleotide therapies, and gene therapy systems. This multidisciplinary group works collaboratively with our discovery and manufacturing colleagues.

We are seeking an exceptional Data Scientist with deep data expertise in the pharmaceutical domain to lead the development and delivery of enterprise-scale data products that power AI-driven insights, process optimization, and regulatory compliance. In this role, you'll bridge pharmaceutical sciences with modern data engineering to transform complex CMC, PAT, and analytical data into strategic assets that accelerate drug development and manufacturing excellence.

Responsibilities:

Data Product Development: Define the roadmap and deliver analysis-ready and AI-ready data products that enable AI/ML applications, PAT systems, near-time analytical testing, and process intelligence across CMC workflows.

Data Archetypes & Modern Data Management: Define pharmaceutical-specific data archetypes (process, analytical, quality, CMC submission) and create reusable data models aligned with industry standards (ISA-88, ISA-95, CDISC, eCTD). 

Modern Data Management for Regulated Environments: Implement data frameworks that ensure 21 CFR Part 11, ALCOA+, and data integrity compliance, while enabling scientific innovation and self-service access. 

AI/ML-ready Data Products: Build training datasets for lab automation, process optimization, and predictive CQA models, and support generative AI applications for knowledge management and regulatory Q&A.

Cross-Functional Leadership: Collaborate with analytical R&D, process development, manufacturing science, quality, and regulatory affairs to standardize data products.

Deliverables include:

• Scalable data integration platform that automates compilation of technical-review-ready and submission-ready data packages with demonstrable quality assurance.

• Unified CMC data repository supporting current process and analytical method development while enabling future AI/ML applications across R&D and manufacturing

• Data flow frameworks that enable self-service access while maintaining GxP compliance and audit readiness

• Comprehensive documentation, standards, and training programs that democratize data access and accelerate product development

Basic Requirements:

• PhD in Computer Science, Data Science, Machine Learning, or AI and 0-5 years of pharmaceutical industry experience; or

• Master of Science in Computer Science, Data Science, Machine Learning, or AI and 5+ years of pharmaceutical industry experience.

• Knowledge of modern data stack technologies (Microsoft Fabric, Databricks, Airflow) and cloud platforms (AWS- S3, RDS, Lambda/Glue, Azure).

• Demonstrated experience designing data products that support AI/ML workflows and advanced analytics in scientific domains.

• Proficiency with SQL, Python, and data visualization tools.

• Experience with analytical instrumentation and data systems (HPLC/UPLC, spectroscopy, particle characterization, process sensors).

• Knowledge of pharmaceutical manufacturing processes, including batch and continuous manufacturing, unit operations, and process control.

• Expertise with data modeling for time-series, spectroscopic, chromatographic, and hierarchical batch/lot data.

• Experience with laboratory data management systems (LIMS, ELN, SDMS, CDS) and their integration patterns.

Additional Preferences:

• Understanding of Design of Experiments (DoE), Quality by Design (QbD), and process validation strategies.

• Experience implementing data mesh architectur