Physicist | Mathematician | Engineer
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Physicist working on quantum gravity, quantum information, and machine learning.
New York University
Master of Science - MS, Mathematics
January 1, 2020 – May 1, 2023
BloomTech
Computer Science
January 1, 2017 – January 1, 2018
University of Maryland
Bachelor of Science - BS, Mathematics
January 1, 2012 – January 1, 2017
Montgomery College
Physics
January 1, 2011 – January 1, 2011
New York University
Doctoral Student
September 1, 2023 – Present
New York, New York, United States · On-site
New York University
Masters Student
September 1, 2020 – May 1, 2023
New York, New York, United States
Software Engineer III
December 1, 2018 – August 1, 2023
New York, New York
Booz Allen Hamilton
Data Scientist
March 1, 2018 – November 1, 2018
Lambda
Assistant Machine Learning Instructor
January 1, 2018 – July 1, 2018
Radiant.Earth
Machine Learning Intern
January 1, 2018 – April 1, 2018
Washington DC-Baltimore Area
University of Maryland College Park
Machine Learning Research Assistant
June 1, 2017 – January 1, 2018
University of Maryland College Park
Lecturer, Organizer
June 1, 2015 – July 1, 2017
University of Maryland College Park
Peer Tutoring Program Director
December 1, 2013 – August 1, 2016
Self-employed
Private Tutor
September 1, 2013 – August 1, 2017
College Park, Maryland, United States
University of Maryland College Park
Mathematical Physics Research Assistant
September 1, 2013 – June 1, 2017
Cultural Fit Analysis
The candidate's diverse background, spanning academic research, teaching, and industry roles at companies like Google and Booz Allen Hamilton, suggests adaptability and a broad perspective. Their pursuit of advanced degrees in theoretical physics indicates a strong inclination towards complex, abstract problem-solving, which can be a significant asset in an ML Engineer role, especially in research-heavy or innovative environments. However, the recent focus on theoretical physics might indicate a preference for academic research over practical, product-focused ML development, which needs to be explored further. The lack of specific project details makes it challenging to fully assess alignment with a collaborative, product-driven culture.
Soft Skills & Operational Fit
The candidate's experience as a lecturer, organizer, and tutor suggests strong communication and mentorship abilities. Their academic pursuits indicate a high level of intellectual curiosity and dedication. The transition from industry to a PhD in theoretical physics and back suggests a strong drive for deep understanding and potentially a preference for challenging, research-oriented problems. However, the lack of specific project details makes it difficult to assess operational fit in a team-based, agile ML engineering environment beyond general academic collaboration.