Job description
Title: 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 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
- Experience with at least one of PyTorch, TensorFlow, Keras, OpenCV, or a similar open-source framework
- Willingness to work as part of a team on complex projects
- Proven leadership and self-direction
Preferred Qualifications
- 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.
