# Laura Bergsten > Staff MLE/data scientist | PhD particle physicist | 📈 Location: Greater Boston, United States Profile: https://flows.cv/laurabergsten I have always been inspired by difficult problems and the potential for big data to solve them. From the smallest components of matter to the complex dynamics of the US healthcare system, data analysis is the key to understanding fundamental systems and building solutions to complicated problems. ## Work Experience ### Staff Machine Learning Engineer @ General Motors Jan 2026 – Present ### Senior Machine Learning Engineer @ General Motors Jan 2024 – Jan 2026 ### Senior Data Scientist @ Motional Jan 2023 – Jan 2024 | Boston, Massachusetts, United States ### Senior Data Scientist @ EQRx Jan 2022 – Jan 2023 | Cambridge, Massachusetts, United States Created cloud-based user-facing tools from end-to-end including scoping, front-end design, database design, data ETL and queries, testing, deployment, and maintenance. These tools led to a rapid decrease in manual time required for routine analyses. Led analyses using machine learning methods for regression, classification, and topic modeling to bring stakeholders data-driven insights which were used to make key business decisions (e.g., pursuing new drug programs in particular disease areas and informing go-forward strategy for marketing and finance teams). ### Data Scientist @ EQRx Jan 2020 – Jan 2022 | Cambridge, Massachusetts, United States Developed expertise in 4+ distinct patient Claims and EHR datasets each including use of numerous medical ontologies and TB-scale relational databases. Rapidly became data science team lead in using this data to provide stakeholders with clear, expeditious analyses which streamlined the process for assessing product markets. Interviewed and onboarded new data scientists and engineers, helping to build a diverse and effective technical team in a growing company. ### Ph.D. Candidate @ Brandeis University Jan 2015 – Jan 2020 | Geneva Area, Switzerland Worked as a lead researcher on a cross-institute multinational team studying a statistically rare decay mode of the Higgs boson in search of new physics. Integrated classical maximum likelihood fits with machine-learning approaches (specifically, boosted decision trees and various neural network architectures) to analyze more than 10 million collision events and classify particle decays with a 1-in-10,000,000,000,000 chance of occurring. Represented the ATLAS collaboration to the general public as a scientific outreach liaison responsible for leading educational tours through the ATLAS detector; Taught four undergraduate lab courses in classical mechanics and electricity and magnetism. ### Research Fellowship @ Brookhaven National Laboratory Jan 2017 – Jan 2018 | New York Awarded a US Department of Energy Office of Science Graduate Student Research fellowship to create, validate, and implement a software suite to operate robotic equipment for the fabrication of detector components for installation on the ATLAS experiment. Managed three summer undergraduates and two PhD students as lead Brandeis researcher at BNL, providing day-to-day supervision and project planning to ensure that major development milestones and timelines were achieved. ### Intern @ General Atomics Jan 2014 – Jan 2014 | Greater San Diego Area Designed an efficient automated computational method for Motional Stark Effect corrections, contributing to R&D on one of the world's largest magnetically confined fusion reactors (the DIII-D experiment). Culminated in a presentation at the American Physics Society: Department of Plasma Physics annual conference in October 2014 and paper published November 2016. ### Intern @ CERN Jan 2014 – Jan 2014 | Meyrin, Switzerland Simulated the effects of radiation damage on longevity of CERN's CMS detector in collaboration with Texas Tech resulting in talk webcast here: ### Intern @ Princeton Plasma Physics Laboratory (PPPL) Jan 2013 – Jan 2013 | Princeton, New Jersey Through the U.S. Department of Energy Science Undergraduate Laboratory Internship program, studied the heating and evaporation of lithium in the LTX experiment, a plasma confinement device. ## Education ### Doctor of Philosophy - PhD in Elementary Particle Physics Brandeis University ### Bachelor of Arts - BA in Double major: Physics, Mathematics Dartmouth College ## Contact & Social - LinkedIn: https://linkedin.com/in/labergsten --- Source: https://flows.cv/laurabergsten JSON Resume: https://flows.cv/laurabergsten/resume.json Last updated: 2026-03-31