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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Advisor, Data Scientist - CMC Data Products - **Company:** Eli Lilly and Company - **Location:** Indianapolis, IN, United States - **Salary:** $126,000.0 - $244,200.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Data Analysis, Microsoft Azure, Health Informatics, Computational Biology, Information Engineering, Data Integration, Data Integrity, Data Security, Data Systems, Data Visualization, Github, Python (Programming Language), Knowledge Management, Laboratory Information Management Systems, Machine Learning, SQL Databases, Data Streaming, Cloudformation, Microsoft Fabric, Information Technology, Data Analytics, Integration Frameworks, Data Management, Machine Learning Operations, GXP, Databricks - **Published:** September 7, 2026 - **Apply:** https://www.biospace.com/logon?PipelinedPage=%2Fjob%2F3072826%2Fadvisor-data-scientist-cmc-data-products%3FAction%3DContinueJobApplication%23application-form ## About the Role * PhD in Computer Science, Data Science, Machine Learning, Biomedical / Medical Informatics, Computational Biology, AI, or closely related STEM degree and 0-5 years of pharmaceutical industry experience; or * Master of Science in Computer Science, Data Science, Machine Learning, AI, Computational Biology, Biostatistics / Applied Statistics / Statistics, or closely related STEM degree 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 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. ## Description 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 ## Related Videos - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) ## Related Articles - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again)