R&D Commercial Product Development Engineer Intern at GAF in Parsippany, NJ

Job description

At GAF, we cover more than just buildings. We cover each other. Under this roof, you will have the tools and resources you need to begin, grow, and develop in your career. Under this roof, you will experience our incredible culture and be empowered to support your teammates, your customers and especially your community through collaboration. Under this roof, we define the future while leading the present through our commitment to continuous improvement and never being satisfied with the status quo. We are GAF. And under this roof, we protect what matters most. Job Title: R&D Commercial Product Development Engineer Intern Introduction: At GAF, we cover more than buildings. We cover each other. No matter what role, tenure, or track, under this roof you are empowered to be there for your teammates, your customers, and especially your community. Under this roof, we don’t back down from hard work– we support one another in pursuit of something bigger. We define the future while leading the present. And under this roof, we own our opportunities. Becoming the market leader only happens when everyone feels they have the opportunity, and the support, to thrive. We are GAF. And under this roof, we protect what matters most. Summary: We are seeking a highly motivated and technically minded candidate for a 3-month internship within our R&D Digital Transformation group. This role bridges experimental formulation chemistry with modern data science, self-service machine learning, and laboratory automation platforms. Working directly alongside formulation scientists and R&D leaders, the intern will aggregate and refine historical trial data, build predictive machine learning models, design adaptive Design of Experiments (DOE) campaigns, and establish structured workflows within our Laboratory Information Management System (LIMS) and Electronic Lab Notebooks (ELN). Essential Duties: Extract, clean, and standardize complex historical formulation datasets from legacy spreadsheets, LIMS, and experimental records. Perform automated statistical analyses to uncover critical ingredient-property relationships. Develop analytical summaries and interactive dashboards to present insights to R&D stakeholders. Train, evaluate, and fine-tune supervised machine learning models to accurately 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 that effectively balance data exploration with practical laboratory validation. Assist in configuring LIMS templates and structured ELN procedures to streamline benchtop data capture. Standardize end-to-end work orders connecting raw material formulations, processing steps, and characterization results into a seamless data pipeline. Support digital knowledge management initiatives by organizing, tagging, and integrating technical literature, reports, and standard operating procedures into central repositories. Under This Roof, We Require Education: Currently pursuing a Bachelor's degree in Chemistry, Chemical Engineering, Materials Science, or a related scientific/engineering discipline. Programming & Software: Proficiency with Python, statistical DOE tools, Google Workspace, and exposure to generative AI platforms. Domain Knowledge: Solid foundational knowledge of general chemistry, material science, polymer chemistry, formulation principles, and analytical characterization. Communication & Collaboration: Strong interpersonal and communication skills with the ability to translate complex data science concepts into clear insights for experimental scientists and leadership. Under This Roof, We Also Value Hands-on experience with LIMS or Electronic Lab Notebook (ELN) platforms.Familiarity with applied machine learning in chemistry or materials science (e.g., ensemble modeling, multi-objective optimization, transfer learning). Practical experience const