LA Galaxy Seeking Associate Data Scientist

This role reports to a growing analytics department under General Manager Will Kuntz and the recently appointed Director of Quantitative Analysis, Ravi Ramineni. Ramineni, a well-regarded figure in soccer analytics, was a key data pioneer with the Seattle Sounders, indicating the Galaxy's serious investment in a data-driven front office. The Associate Data Scientist will be tasked with building statistical and machine learning models to support player recruitment and valuation. Key responsibilities include preparing datasets for player evaluation, creating visualizations to help the recruitment team, and analyzing opponent tendencies to inform tactical planning. This position reflects a league-wide trend; most MLS clubs now have multiple full-time analytics staffers. Since 2020, every MLS stadium has been equipped with Second Spectrum's optical tracking systems, which capture the real-time coordinates of every player 25 times per second, generating a massive dataset for analysis. For students building a portfolio, projects that mirror the job's duties are key. Developing an "Expected Goals (xG)" model using Python, creating NBA-style shot charts for soccer to identify player strengths, or analyzing player performance data to simulate match outcomes are all relevant experiences. The technical toolkit for such roles typically includes Python (with libraries like scikit-learn), experience with data visualization tools, and a strong foundation in machine learning techniques such as classification and clustering models. While direct experience with soccer-specific data is preferred, it is not always a strict requirement for entry-level positions. Career paths in sports analytics are expanding beyond entry-level roles. In recent years, analytics leaders have been promoted to high-level positions like Assistant General Manager at clubs such as D.C. United and Nashville SC, demonstrating a clear route for advancement and influence within front offices.