Role Description The Utah Jazz are looking for a Data Scientist to perform modeling and analysis that will help inform basketball decision-making. We are located in Salt Lake City, but are open to remote candidates (must be legally authorized to work in the United States). Duties & Responsibilities - Develop and maintain models to better predict basketball outcomes and facilitate better decision-making across Basketball Operations. - Perform ad hoc data analysis as necessary to help basketball decision-makers. - Interface with all departments within Basketball Operations (scouting, coaching, analytics, sports science) and strive to understand and meet their needs. - Identify, research, and implement opportunities for new models, data sources, and basketball analytics techniques. - Help maintain and test datasets used to perform analysis. - Maintain best practices for software development including test writing, documentation, and adherence to and improvement of coding standards. - Protect the reputation of the company. - Exemplify the Utah Jazz mission and values: transparent, all in, community obsessed, one team, and scrappy. - Accept other duties, as assigned. Competencies - Communicates Effectively – Develops and delivers multi-mode communications that convey a clear understanding of the unique needs of different audiences. - Demonstrates Self-awareness – Uses a combination of feedback and reflection to gain productive insight into personal strengths and weaknesses. Qualifications - Bachelor’s in Statistics, Data Science, Math or equivalent experience. - 2+ years of experience analyzing data and training statistical models in a production environment using Python or R. - 2+ years of experience with basketball data and the latest basketball analytics techniques. - 2+ years of experience with data visualization. - Expertise in statistics fundamentals and state-of-the-art predictive modeling techniques. - A desire to work in a startup-like environment where you will be empowered to make design decisions. - A track record of writing clean and well-documented code. - Enthusiasm for basketball. - Nice to have experience with MLOps. - Nice to have experience with SQL. - Nice to have experience with data engineering. - Must be legally authorized to work in the United States. Physical Demands - This person must be able to communicate and express themselves both written and verbally. - This person must be able to observe, inspect, estimate, and assess. - This person must be able to sit for long periods of time.

Lead end-to-end analytics projects, develop complex analyses, and collaborate with cross-functional teams to translate needs into measurable outcomes. Mentor junior analysts and create impactful visualizations to communicate insights effectively.

• Ejecutar queries en SQL para extracción, filtrado y agregación de datos. • Analizar campañas, estudios y proyectos, convirtiendo datos en hipótesis, aprendizajes y oportunidades claras. • Limpieza, modelado y análisis de datasets complejos en Excel / Google Sheets. • Definir, proponer y justificar la metodología más adecuada según el brief del cliente y las restricciones de tiempo/presupuesto. • Análisis de resultados y elaboración de reportes estratégicos, con conclusiones sólidas y recomendaciones de negocio claras. • Construir y presentar narrativas de alto impacto: no solo qué pasó, sino por qué importa y qué hacer. • Responder preguntas de negocio complejas de equipos internos y clientes. • Gestión total de los proyectos: gestión de tiempos, prioridades y stakeholders. • Orientación a resultados: buscar soluciones creativas incluso cuando los datos o herramientas no son perfectos.

• Ejecutar queries en SQL para extracción, filtrado y agregación de datos. • Analizar campañas, estudios y proyectos. • Limpieza, modelado y análisis de datasets complejos. • Definir y justificar la metodología más adecuada. • Análisis de resultados y elaboración de reportes estratégicos. • Construir y presentar narrativas de alto impacto. • Responder preguntas de negocio complejas. • Gestión total de los proyectos: tiempos, prioridades y stakeholders.