Data Scientist

Job Description

Position Summary: The Data Scientist, Basketball Data Strategy is a critical member of the Basketball Data Strategy team. Reporting to the Senior Data Scientist, Basketball Data Strategy, they will develop, test, and maintain predictive models and metrics for player evaluation, roster construction, player development, and in-game strategy. They will work with the Development team to create data visualizations and applications to assist in the consumption of results, and with the Data Engineering team to perform crucial data maintenance and upkeep of models and metrics. The Data Scientist will primarily work under the direction of the Senior Data Scientist and collaborate with other members of the Basketball Data Strategy team to provide tools needed for team success.

Essential Functions (Duties & Responsibilities**):

• Utilize statistical analysis, machine learning, and predictive modeling techniques to provide solutions for Basketball Operations
• Drive development of production-level models — from exploratory data analysis and problem framing all the way through model validation and deployment
• Integrate novel research, basketball metrics, and models into existing and new applications
• Originate innovative ways to analyze / summarize data from existing and new stakeholders (e.g. G League, Player Health, NBA Draft)
• Operate in both the ad-hoc and the long-term, creating new research and sustainable solutions as necessary in collaboration with the rest of the Basketball Operations department
• Manipulate and leverage large data for analysis and modeling; source, process, and understand new datasets that may aid in potential evaluations
• Translate data-driven results into actionable basketball decisions and recommendations
  

Education

• Bachelor’s in Statistics, Computer Science, or a related field, or equivalent academic or professional experience
  

Minimum Qualifications

• 3+ years of professional experience in a data science or machine learning role
• Proficiency in Python or equivalent language for end-to-end model development
• Experience using modern machine learning and deep learning techniques (e.g. scikit-learn, TensorFlow) to build robust models
• Expertise with techniques to analyze and use large data sets (e.g. PySpark, SQL); willingness to work with geolocation tracking data and biomechanical data
• Familiarity with distributed computing and cloud-first principles
• Ability to write clean and efficient code; proficiency with version control and code modularization
• Strong communication skills
• Experience with time series/probabilistic forecasting, MLOps and GPU computing is a plus

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