CMC AI/ML & Automation Scientist for Drug Development

100 Eli Lilly and Company; United States; On-site; USD 129,000 - 209,000; Full time

Benefits offered by this job

401(k) plan

Pension

Vacation benefits

Well-being benefits

Job summary

Lilly in Indianapolis, IN, USA is hiring a CMC AI/ML and Automation scientist to collaborate across Product R&D to derive insights from data and accelerate drug development. Candidates should have deep technical depth and leadership and be ready to apply AI/ML in CMC disciplines.

Responsibilities include building decision support tools, dashboards, and autonomous workflows, using LLMs to improve data science, and working with lab scientists to deploy automation.

Qualifications

• PhD in Chemical Engineering, Chemistry, Formulation, Analytical Sciences, or related field (or MS with 5+ years).
• Proficient in Python; ability to quickly learn new languages/tools.
• Experience with AI frameworks (TensorFlow, PyTorch).
• Experience applying data science/AI to varied data types and LLMs.

Responsibilities

• Identify problems across Product R&D using AI/ML and engineering principles.
• Build decision support tools and dashboards for high-frequency decisions.
• Apply LLM advancements to accelerate development workflows.
• Develop AI-driven autonomous design and data-rich experiments.
• Create lab automation protocols for closed-loop workflows.
• Stay current with AI in CMC development.

Skills

Python

AI/ML

Data science

LLMs

Communication

Analytical skills

Collaboration

Education

PhD in Chemical Engineering

MS in Chemical Engineering (with 5+ years)

Tools

TensorFlow

PyTorch

SQL

Job description

Lilly in Indianapolis, IN, USA is hiring a CMC AI/ML and Automation scientist to collaborate across Product R&D to derive insights from data and accelerate drug development. Candidates should have deep technical depth and leadership and be ready to apply AI/ML in CMC disciplines.

Responsibilities include building decision support tools, dashboards, and autonomous workflows, using LLMs to improve data science, and working with lab scientists to deploy automation.