Player 15 Group Data Scientist

Organization:Player 15 Group

Position Title:Data Scientist

Reports to:Senior Director Business Intelligence & Data Strategy

Location:Phoenix AZ

Search Contact:Prodigy Search

BACKGROUND

Player 15 Group the sports and entertainment company behind the Phoenix Suns (NBA) Phoenix Mercury (WNBA) Valley Suns (G League) and Mortgage Matchup Center is redefining the industry standard for how organizations engage fans partners and their communities. Headquartered in downtown Phoenix and reaching audiences around the world Player 15 Group is driven by possibility innovation and a relentless commitment to creating memorable moments on and off the court. The organizations culture is anchored in purpose-driven leadership and fueled by individuals who bring passion creativity and vision to everything they do. By challenging convention amplifying diverse voices and crafting experiences that resonate well beyond the final buzzer Player 15 Group is where talent meets purpose and bold ideas become reality.

SUMMARY

The Data Scientist will be a key member of the Business Intelligence & Data Strategy team leading advanced analytics initiatives that support revenue growth fan engagement and organizational decision-making across Player 15 Group. This individual will design build and deploy predictive and prescriptive models that inform ticketing marketing partnerships and customer strategy transforming complex data into insights that drive measurable business outcomes.

Serving as both a technical expert and strategic thought partner the Data Scientist will collaborate closely with stakeholders across the business to forecast demand optimize pricing enhance retention and identify high-value audiences. The ideal candidate brings hands-on experience with modern data science tools and techniques thrives in a fast-paced team-oriented environment and is highly skilled at communicating findings in a clear compelling and actionable way for executive and non-technical audiences.

RESPONSIBILITIES

• Lead the development of predictive models to forecast demand and sales that support revenue planning ticket pricing and inventory optimization.
• Design build and maintain retention models for season ticket holders suite holders and marketing partners to proactively identify renewal and churn risk.
• Develop lifetime value models and methodologies that identify high-value fans and inform personalized engagement offers and communication strategies.
• Build and refine audience segmentation and clustering frameworks that power decision-making across ticketing marketing and partnerships.
• Leverage Python and SQL to extract transform and analyze data from multiple sources ensuring datasets are accurate consistent and ready for modeling.
• Partner with data engineers IT and BI analysts to operationalize models into the data warehouse dashboards and reporting ecosystem.
• Collaborate with ticketing marketing and partnership teams to translate business questions into analytical approaches and data products.
• Communicate analytical findings and model outputs through clear storytelling visualizations and presentations tailored to executive and cross-functional audiences.
• Establish and track model performance using metrics such as MAPE MAE RMSE R² AUC and other relevant KPIs; recommend enhancements based on results.
• Design and evaluate A/B tests and experiments to measure the impact of new strategies campaigns and pricing or packaging decisions.
• Implement best practices in feature engineering feature selection cross-validation and model versioning to ensure reliability and repeatability.
• Develop and maintain dashboards and visualizations using tools such as Tableau Power BI or Looker to support self-service analytics.
• Work within cloud data warehouse environments (e.g. Redshift Snowflake) and contribute to ETL/ELT processes in partnership with engineering teams.
• Stay current on emerging data science techniques tools and industry trends; introduce new approaches that advance Player 15 Groups analytics capabilities.
• Support on-site needs as required including occasional presence at games or events to understand operations fan behavior and partner activation.

QUALIFICATIONS

• 35 years of experience in applied analytics data science or a related quantitative field.
• Bachelors degree in Data Science Statistics Economics Applied Mathematics or a related quantitative discipline.
• Proficiency in Python (e.g. pandas NumPy scikit-learn XGBoost matplotlib) and SQL for data transformation modeling and analysis.
• Demonstrated experience building and deploying forecasting and predictive models (e.g. regression ensemble methods) to predict sales renewals and customer demand.
• Strong understanding of statistical analysis machine learning model evaluation and predictive modeling techniques.
• Experience working with model evaluation metrics such as MAPE MAE RMSE R² and AUC and connecting these metrics to business impact.
• Knowledge of A/B testing hypothesis testing and experimental design to optimize performance of campaigns pricing and product strategies.
• Hands-on experience with feature engineering feature selection cross-validation and model lifecycle management best practices.
• Experience developing segmentation and clustering models to explain and predict audience behavior.
• Familiarity with data visualization tools such as Tableau Power BI or Looker.
• Experience working in data warehouse environments (e.g. Redshift Snowflake) and with ETL/ELT tools such as dbt Airflow or similar.
• Proven ability to work cross-functionally with ticketing marketing partnerships or similar business teams to drive data-informed strategies.
• Background in sports entertainment or consumer analytics preferred but not required.
• Strong communication skills with the ability to translate complex analyses into clear concise and actionable insights.
• Ability to work non-traditional hours including evenings and weekends as needed for games and events.
• Ability to move throughout an arena environment for extended periods and engage in standard office tasks; must be able to converse effectively in person and by phone.

CONTACT INFORMATION

Founded in 2007 Prodigy Search located in the New York City suburb of Freehold NJ boasts over 80 years of experience in the sports and entertainment business. As a renowned nationwide leader in senior-level executive search Prodigy Search has honed its business principles and expertise establishing itself as the largest boutique recruiting agency in North America. We recruit with integrity purpose and passion.

No phone calls please. For any additional questions please email.