R&D Commercial Product Development Engineer Intern

GAF•Parsippany, NJ

10d

About The Position

GAF is seeking a highly motivated, technically minded intern for a 3-month position within its R&D Digital Transformation group. The role bridges experimental formulation chemistry with data science, self-service machine learning, and laboratory automation. Working with formulation scientists and R&D leaders, the intern will refine historical trial data, develop predictive models, design adaptive Design of Experiments (DOE) campaigns, and support structured workflows in Laboratory Information Management Systems (LIMS) and Electronic Lab Notebooks (ELN).

Requirements

Currently pursuing a bachelor's degree in Chemistry, Chemical Engineering, Materials Science, or a related scientific or engineering discipline.
Proficiency with Python, statistical DOE tools, and Google Workspace.
Exposure to generative AI platforms.
Foundational knowledge of general chemistry, materials science, polymer chemistry, formulation principles, and analytical characterization.
Strong interpersonal and communication skills, including the ability to explain complex data science concepts clearly to experimental scientists and leadership.

Nice To Haves

Hands-on experience with LIMS or Electronic Lab Notebook (ELN) platforms.
Familiarity with applied machine learning in chemistry or materials science, such as ensemble modeling, multi-objective optimization, or transfer learning.
Practical experience constructing agentic workflows or automated solution pipelines.
Prior wet-lab or industrial chemistry laboratory experience.

Responsibilities

Extract, clean, and standardize historical formulation datasets from legacy spreadsheets, LIMS, and experimental records.
Perform automated statistical analyses to identify ingredient-property relationships.
Prepare analytical summaries and interactive dashboards for R&D stakeholders.
Train, evaluate, and fine-tune supervised machine learning models to predict physical, mechanical, and rheological material properties.
Apply inverse prediction algorithms, optimization techniques, and grid searches to identify candidate formulation recipes.
Design multi-run DOE test protocols balancing data exploration with laboratory validation.
Assist with configuring LIMS templates and structured ELN procedures for benchtop data capture.
Standardize end-to-end work orders connecting raw material formulations, processing steps, and characterization results into a data pipeline.
Organize, tag, and integrate technical literature, reports, and standard operating procedures into central repositories.