Advisory, Data Scientist – CMC Data Products Full-time

Data Scientist

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.

Organizational & Position Overview: The Bioproduct Research and 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 include:

• 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:

• Master's degree in Computer Science, Data Science, Machine Learning, AI, or related technical field.
• 8+ years of product management experience focused on data products, data platforms, or scientific data systems and a strong grasp of modern data architecture patterns (data warehouses, data lakes, real-time streaming).
• 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 in 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 architectures in scientific organizations.
• Knowledge of MLOps practices and model deployment in validated environments.
• Familiarity with regulatory submissions (eCTD, CTD) and how analytical data supports marketing applications.
• Experience with CI/CD pipelines (GitHub Actions, CloudFormation) for scientific applications.