Data Scientist

Data Scientist, Analytics Strategy

The Data Scientist, Analytics Strategy is a critical member of the Analytics Strategy team, responsible for developing, testing, and maintaining predictive models and metrics for player evaluation, roster construction, player development, and in-game strategy. They will work with the Software Development team to create data visualizations and applications to assist in the consumption of results, and with the Data Engineering team to perform data onboarding and updating of models and metrics. The Data Scientist will collaborate with the other Data Scientists on the Analytics Strategy team to drive predictive value to aid decision-making across the entirety of Basketball Operations.

Utilize statistical analysis, machine learning, and predictive modeling techniques to provide solutions and predictive power for Basketball Operations

Drive development of production-level models — from exploratory data analysis and problem framing through model validation and deployment

Integrate novel research, basketball metrics, and models into existing and new applications; onboard and evaluate new data vendors

Originate innovative ways to analyze / summarize data from internal 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

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

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, PyMC) 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

Experience with version control (e.g. Git)

Best judgment on role and prudent use cases of AI to augment data science workflows

Adaptable, eager to self-learn new technical skills

Knowledge of and passion for NBA basketball

El Segundo (office M‐F), and other occasional off‐site events

Less than 5% of the time

Full-time. Must be available to work evenings, weekends, and holidays as reasonably required

The pay range for this role is $100,000 - $115,000 annually. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience and certifications. In addition to those factors, we consider the relative pay of our current employees in similar positions when making a final offer.

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.