Senior Data Scientist (Computer Vision, Machine Learning) at Hudl
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Imperial College London
PhD, Computer Vision
January 1, 2006 – January 1, 2010
Delft University of Technology
M.Sc., Computer Science
January 1, 2003 – January 1, 2005
Aristotle University of Thessaloniki (AUTH)
Ptychion, Electrical & Computer Engineering
January 1, 1996 – January 1, 2002
Hudl
Senior Data Scientist (Computer Vision, Machine Learning)
October 1, 2018 – Present
Hudl
Data Scientist (Computer Vision, Machine Learning)
January 1, 2018 – October 1, 2018
Hudl
Computer Vision Engineer
August 1, 2015 – December 1, 2017
realeyesit.com
Research Scientist
September 1, 2013 – July 1, 2015
Imperial College London
Research Associate
October 1, 2009 – July 1, 2013
Probabilistic Graphical Models 1: Representation
Coursera
June 24, 2026 – Present
Measure What Matters: Succeeding with Objectives and Key Results (OKRs)
June 24, 2026 – Present
Probabilistic Graphical Models Specialization
Coursera
June 24, 2026 – Present
Probabilistic Graphical Models 3: Learning
Coursera
June 24, 2026 – Present
Probabilistic Graphical Models 2: Inference
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background is heavily skewed towards Computer Vision and Machine Learning, which are specialized areas within data science. While these skills are valuable, the target role is 'Data Analyst'. This suggests a potential mismatch in focus, as a Data Analyst role typically emphasizes business intelligence, reporting, and statistical analysis over advanced ML model development. The lack of diverse project experience outside of CV/ML makes it difficult to assess broader analytical capabilities relevant to a general Data Analyst role. The academic background and research roles indicate a strong inclination towards deep technical problem-solving, which may or may not align with the day-to-day responsibilities of a Data Analyst.
Soft Skills & Operational Fit
The candidate's extensive experience in research and senior data science roles suggests strong analytical thinking, problem-solving, and potentially leadership skills. However, without specific project descriptions or interview data, it is difficult to assess communication, teamwork, and adaptability directly. The certifications in OKRs suggest an understanding of goal-setting and operational frameworks.