Keith Landry
Data Analystdata science at VideoAmp
VideoAmp
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
data science at VideoAmp
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
UCLA
Doctor of Philosophy (Ph.D.), Physics
January 1, 2009 – January 1, 2016
University of Massachusetts Amherst
Bachelor's degree, Physics
January 1, 2005 – January 1, 2009
VideoAmp
Director Data Science
September 1, 2025 – Present
VideoAmp
Senior Principal data scientist
October 1, 2024 – September 1, 2025
VideoAmp
Principal data scientist
October 1, 2021 – October 1, 2024
VideoAmp
Senior Data Scientist
December 1, 2019 – October 1, 2021
Genius Sports
Senior Data Scientist - machine learning
July 1, 2018 – December 1, 2019
Genius Sports
Data Scientist/Machine Learning Engineer
August 1, 2017 – June 1, 2018
Centerfield Corporation
Data Science Intern
February 1, 2017 – July 1, 2017
University of California, Los Angeles
Doctoral Candidate in physics
September 1, 2009 – December 1, 2016
Cultural Fit Analysis
The candidate has a consistent career path within data science, primarily at two companies (VideoAmp and Genius Sports), which suggests loyalty and ability to grow within an organization. The progression to Director Data Science indicates leadership and strategic thinking. However, the target role of 'Data Analyst' is a significant step down from 'Director Data Science', which might indicate a mismatch in career aspirations or a desire for a different type of challenge. The lack of project diversity in the provided data makes it difficult to fully assess breadth of experience outside of their primary roles.
Soft Skills & Operational Fit
The candidate's career progression from individual contributor to director-level roles suggests strong leadership, problem-solving, and project management skills. The academic background in physics implies a rigorous approach to data and problem-solving. However, specific details on communication and collaboration from the provided data are limited.