Chemistry Data Science Intern - Digital CMC Workflows

Zoetis • Kalamazoo, MI

8d

About The Position

Zoetis VMRD Process Chemistry is seeking a summer intern to help build and test a structured digital approach to managing process chemistry and analytical data. The intern will organize reaction, batch, purity, and impurity data from an active development program in a structured CMC data platform, develop an impurity-fate case study, and benchmark the platform against the current workflow. The work will produce a data-backed recommendation on how digital CMC tools should be deployed across the portfolio.

Requirements

• Currently enrolled in a Bachelor’s, Master’s, or PhD program in Chemistry, Cheminformatics, Chemical Engineering, Data Science, Computational Chemistry, Analytical Chemistry, or a closely related field.
• Coursework in organic and analytical chemistry.
• Sufficient organic chemistry background to correctly interpret reaction schemes, HPLC/chromatographic data, and impurity profiles.
• Demonstrated experience analyzing structured or semi-structured data using Python, R, SQL, Excel, or Power BI; no specific language is required, and reproducible data handling is emphasized.
• Strong attention to data quality, traceability, and reproducibility, including documenting assumptions and flagging gaps rather than filling them in.
• Clear written and verbal technical communication skills, including the ability to present findings and defend conclusions to scientists and non-chemist stakeholders.

Nice To Haves

• Rising seniors and graduate students are preferred.

Responsibilities

• Structure, clean, and validate reaction, batch, purity, and impurity data from an assigned process chemistry project, using consistent compound and impurity naming conventions.
• Build an impurity-fate and data-retrieval case study in a structured CMC data environment, such as Luminata or an equivalent platform, mapping impurity formation, carry-through, and purge across the synthetic route.
• Design and execute a benchmark comparing defined scientific tasks in the digital environment with the current workflow, measuring time to correct answer, accuracy, traceability, data completeness, and number of systems consulted.
• Analyze benchmark results and create a concise KPI scorecard summarizing where structured data delivers measurable gains and where it does not.
• Document data standards, assumptions, and gaps, and deliver a final presentation with recommendations for broader deployment to Process Chemistry, Analytical, and Tech & Digital stakeholders.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: No Education Listed