About the role
About
the Role The Data Scientist for League Insights supports the Senior Director, League Strategy and Insights in building the analytical and visualization infrastructure that powers decision-making across Overtime Elite (OTE). This role sits at the intersection of data science, basketball operations, talent evaluation, and league systems—translating complex datasets into intuitive tools and insights that help OTE evaluate players and teams, identify talent, project future outcomes, understand competition, and tell more compelling stories about on-court performance. The goal of this role is to build scalable intelligence systems that improve how the league evaluates and recruits talent, assesses teams and competition, supports team performance, and communicates the stories that deepen fan understanding of OTE. This is a hands-on, mid-level role for someone who can independently own defined analytical work streams while collaborating closely with basketball, operations, content, broadcast, and technical stakeholders. As part of a small league office team, this person’s work will directly inform how OTE evaluates players and teams, structures competition, identifies opportunities for growth, and shapes the future direction of the league. Success in this role requires someone who can bring structure to evolving needs, learn new tools quickly, and deliver practical solutions in a fast-moving sports environment.
What You'll Do League Intelligence Systems & Dashboards Design and maintain interactive data platform that translates league, team, and player data into actionable insights for coaches, teams, league staff, and leadership Develop intuitive visualization tools that simplify complex performance, evaluation, and competition data for non-technical users Own defined components of scalable analytics products that support league-wide decision-making, talent evaluation, and team performance analysis Ensure analytics products are accessible, reliable, and operationally useful across the league ecosystem Data Integration & League Infrastructure Develop and maintain reliable processes for integrating data from multiple league systems into a unified analytics environment Maintain clean, structured datasets that support reporting, evaluation, modeling, and analysis Partner with League Operations to ensure accurate and consistent data flow across statistical, game, performance, and operational systems Improve data reliability and reduce manual intervention across recurring analytical workflows Talent Evaluation, Predictive Modeling & Competitive Insights Build, test, and refine statistical and predictive models that support player evaluation, talent identification, team analysis, and competitive forecasting Develop frameworks that help OTE identify high-potential talent and project players across roles, competition levels, and future environments Analyze team quality, roster strength, performance trajectories, and competitive context to inform recruiting, league participation, and growth decisions Improve how OTE compares players and teams across leagues, circuits, schedules, roles, and levels of competition Build benchmarks that support player and team evaluation while clearly communicating projected outcomes and the factors influencing them Performance & Development Analytics - Team Enablement Create visual frameworks and reporting that help teams understand performance trends, development opportunities, and results relative to league standards Translate statistical and observational data into actionable insights for coaching and operations staff Build position-, role-, and competition-based benchmarks that support player evaluation, roster planning, recruiting, and team-level decision- making Analytics Storytelling, Reporting & Insight Delivery Generate recurring performance and league intelligence reports for leadership, operations, teams, content, and broadcast partners Identify trends, milestones, comparisons, and performance narratives that explain the success and development of OTE players and teams Partner with content and broadcast teams to provide analytical context that strengthens coverage, storytelling, and audience understanding Develop insights that help fans understand player impact, team identity, competitive context, and the broader stories shaping the league Ensure analytical findings are clearly communicated and usable across both internal decision-making and external storytelling Systems Improvement & Automation Develop and improve automated workflows for data processing, reporting, modeling, and performance tracking Use AI-assisted tools for coding, analysis, documentation, and recurring operational workflows while validating outputs for accuracy and reliability Create lightweight, repeatable processes that improve consistency, handoffs, scalability, and the usability of league analytics systems