Lead Quantitative Analyst, Computer Vision

AI overview

Develop and implement advanced computer vision models to enhance player evaluation and development at the Phillies, leveraging cutting-edge methodologies and team collaboration.

Title: Lead Quantitative Analyst, Computer Vision
Department: Baseball Research & Development
Reports to: Director, Predictive Modeling
Status: Regular Full-Time
Location: Philadelphia, PA; also open to Remote

Job Description

As a Lead Quantitative Analyst, Computer Vision, you help shape the future of Phillies Baseball Operations by building computer vision models and pipelines to construct predictive metrics and output that improve our player evaluation forecasts and player development capabilities. Using cutting-edge computer vision methodologies and techniques, you weaponize the depth and breadth of video available across all player performance settings by extracting meaningful signal for downstream models and analysis. Join a team doing cutting-edge research on problems throughout the game of baseball, with the unique opportunity to add your computer vision skillset to the team

Responsibilities

  • Construct computer vision models using available image and video data to extract signal for use in predictive models and other analyses
  • Communicate with software engineers and other quantitative analysts to ensure computer vision model output aligns with the needs of downstream models and applications
  • Collaborate with departmental leadership to maintain and grow our computer vision research roadmap
  • Work with our Infrastructure and Machine Learning Engineering team to build robust, scalable, and maintainable pipelines for computer vision solutions

Required Qualifications

  • Possess or are pursuing a BS, MS or PhD in Machine Learning, Computer Science, or related or equivalent practical experience
  • Demonstrated experience or a public portfolio of applied computer vision work leveraging open-source frameworks such as PyTorch, TensorFlow, Keras, OpenCV, etc.
  • Willingness to work as part of a team on complex projects
  • Proven leadership and self-direction

Preferred Qualifications

  • Familiarity with best practices in machine learning operations (Git, Docker, MLFlow or the equivalent)
  • Knowledge of the state of public baseball analytics research

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, age, disability, gender identity, marital or veteran status, or any other protected class.

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