Mollie Conrad

Background

About Mollie Conrad

Mollie Conrad, MSc.(she/her) is a Senior Data Scientist and Python developer with over a decade of experience working across data engineering, machine learning, deep learning, and scientific and industrial process modelling. She enjoys building systems that turn complex, high-dimensional data into clear, actionable insight. She has worked with two industrial optimization companies, where she delivered more than 20 proof-of-concept projects that converted into recurring revenue and contributed directly to ARR growth. Her work has spanned everything from data ingestion and large-scale exploratory analysis to custom ML solutions deployed in production environments. Mollie holds a BSc and MSc in Astrophysics and Gravitation from the University of Waterloo (Department of Physics and Astronomy). During her graduate research under Dr. Michel Fich, she developed a computational simulation of intermediate-mass star formation and served as lead author on the resulting publication and her Master’s thesis. She also collaborated with Dr. Michael Balogh on the Mauna Kea Spectroscopic Explorer (MSE), where she developed an optical-fiber allocation algorithm (an early example of her interest in solving complex optimization problems at scale).At her core, Mollie is deeply curious and committed to continuous learning; she is always exploring new tools, techniques, and courses to expand her skillset. She takes particular pride in creating clear documentation and structured processes, and she enjoys teaching, mentoring, and supporting early-career data scientists as they grow into confident practitioners.Press Release (University of Waterloo): The origin of water on planets in our universe https://educationnewscanada.com/article/education/level/university/1/891977/the-origin-of-water-on-planets-in-our-universe.html Conrad, M. E, & Fich, M, 2020. “Water in the Star-forming Region NGC 7129 FIRS 2”, ApJ, 890, 178 Conrad, M, 2018. H₂¹⁸O and H₂¹⁶O in Intermediate Mass Star Formation Regions. M.Sc. Thesis, University of Waterloo, UWSpace. http://hdl.handle.net/10012/13855