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AVP, Lead Data Scientist
PhD machine learning scientist with over 16 years of experience in applied research & machine learning, including computer vision & natural language processing. 13-plus years of experience working with large data-sets to enable data science & business objectives. Demonstrated ability to efficiently implement machine learning algorithms utilizing deep convolutional and recurrent neural networks to solve business challenges. Supervised a series of more junior employees at several organizations. Exemplary programming skills with Python as the main language, with working knowledge of other languages. Excellent written and verbal communication abilities as indicated by: - 12 peer-reviewed first-author publications in some of the most prestigious journals in astronomy - well-received presentations to technical audiences at dozens of astronomy conferences - extensive experience presenting to the general public during astronomy outreach events at the University of Toronto, MIT and Boston University.
University of Toronto
Doctor of Philosophy (Ph.D.), Astronomy and Astrophysics
January 1, 2006 – January 1, 2011
The University of British Columbia
Bachelor's Degree, Physics
January 1, 2001 – January 1, 2006
Chubb
AVP, Lead Data Scientist
February 1, 2023 – Present
Toronto, Ontario, Canada
Savormetrics
Lead Computer Vision Scientist
June 1, 2022 – January 1, 2023
Lightyear Health
Senior Machine Learning Scientist
February 1, 2021 – May 1, 2022
Savormetrics
Senior Data Scientist
November 1, 2020 – February 1, 2021
Paravision
Machine Learning Researcher
January 1, 2018 – June 1, 2020
Toronto
RBC
Data Scientist
April 1, 2017 – October 1, 2017
Toronto, Canada Area
Savormetrics
Senior Data Scientist
January 1, 2017 – April 1, 2017
Boston University
Senior Postdoctoral Associate
March 1, 2015 – August 1, 2016
Massachusetts Institute of Technology (MIT)
Astronomer - Sagan Postdoctoral Fellow
September 1, 2011 – August 1, 2014
Cambridge, MA, USA
Cultural Fit Analysis
The candidate's background is heavily skewed towards Data Science, Machine Learning, and academic research (Astronomy/Astrophysics). While these fields require strong analytical and problem-solving skills, the direct alignment with a 'Backend Engineer' role is moderate. The breadth of skills is strong within the ML/Data Science domain, but there is limited explicit mention of traditional backend engineering skills such as API design, distributed systems, specific backend frameworks (e.g., Spring, Node.js, Django/Flask for web), or cloud infrastructure (AWS, GCP, Azure). The project diversity is primarily within ML application development. This suggests a potential cultural fit for a backend role that is heavily focused on ML infrastructure or serving, but less so for a general-purpose backend engineering position.
Soft Skills & Operational Fit
The candidate demonstrates strong leadership, coaching, and management skills through various roles. Experience in R&D and product development suggests an ability to contribute to innovation and practical application. The seminar series initiative at Paravision highlights a proactive approach to knowledge sharing and peer review, indicating a collaborative and quality-focused mindset. However, the resume does not provide specific details on communication style or stress handling, which would typically be assessed in psychometric tests or interviews.