Basketball Data Scientist, Phoenix Suns

Player 15 Group; Phoenix (AZ); On-site; USD 110,000 - 150,000; Full time

Job summary

Player 15 Group is seeking a Basketball Data Scientist to turn complex basketball questions into rigorous analysis, models, and decision-ready recommendations. You will work with Basketball Analytics, Engineering, and Basketball Operations on high-impact research and data products that influence front office, coaching, scouting, and player development.

The role emphasizes building predictive models, developing tools, and communicating results to diverse stakeholders.

Qualifications

• Solid foundation in data science and analytics applied to basketball or sports.
• Ability to translate complex basketball questions into analytical plans and actionable insights.
• Proficiency in Python or R for data analysis, modeling, and reporting.

Responsibilities

• Own high-impact basketball data science and analysis initiatives.
• Translate ambiguous basketball questions into clear analytical plans, research designs, models, tools, and recommendations
• Conduct basketball research using statistical modeling, ML, exploratory analysis to uncover insights
• Build, validate, and maintain models evaluating players, teams, lineups, tactics, and decisions
• Write clean, reproducible code in Python or R for analysis and workflows
• Develop tools, dashboards, and data products to turn research into decisions
• Partner with Engineering to productionalize model outputs and data products
• Document methods and limitations for reuse by the analytics team
• Collaborate with basketball stakeholders to connect analysis to decisions
• Stay current with research and methods; explore new approaches
• Build reusable workflows with Engineering to scale prototypes
• Integrated work with Analytics leadership and Engineering teams
• Support front office, coaching, scouting, and player development through timely analysis
• Translate findings into practical recommendations for decision-makers

Skills

Basketball curiosity

Technical rigor

Practical builder

Clear communicator

Ownership mindset

Collaborative teammate

Data science foundation

Basketball analytics experience

Python

R

SQL

Tools

Python

R

SQL

Job description

At Player 15 Group, we aren't just building a team: we're crafting a legacy.

We’re a collective of visionaries who consistently put people first, knowing that true greatness is forged through shared passion and purpose. Service focused to our core, we’re driven by an insatiable desire to innovate and elevate every experience. Our strength lies in unity, where diverse talents converge to achieve extraordinary feats, and we infuse every endeavor with genuine joy. Here, a relentless pursuit of growth isn't just encouraged—it's the very air we breathe. If you're ready to transcend expectations, unlock unparalleled potential, and dedicate your energy to something truly special, your moment to make an indelible mark on the future of sports and live entertainment starts now.

The Basketball Analytics team partners across Basketball Operations to transform data, research, and technology into actionable insights for front office, coaching, scouting, player development, and basketball strategy stakeholders.

As a Basketball Data Scientist, your practical day-to-day and forward-thinking work will focus on turning complex basketball questions into rigorous analysis, clear models, useful tools, and decision-ready recommendations. Working closely with Basketball Analytics, Engineering, and Basketball Operations, you will own high-impact research and projects that connect technical rigor with real basketball insights.

What You Will Do

• Own high-impact basketball data science and analysis initiatives.

• • Translate ambiguous basketball questions into clear analytical plans, research designs, models, tools, and recommendations
  • Conduct basketball research using statistical modeling, machine learning, exploratory analysis, and domain expertise to uncover actionable insights
  • Build, validate, and maintain models that help evaluate players, teams, lineups, tactics, and basketball decision-making questions
  • Work with large and complex basketball datasets, including tracking/spatiotemporal data, play-by-play, event data, lineup/personnel data, scouting information, and other internal sources

• Develop tools, workflows, and data products that turn research into decisions.

• Write clean, reproducible code in Python or R for analysis, modeling, reporting, and internal workflows
• Build internal tools, dashboards, visualizations, and workflows when needed to move projects forward quickly
• Partner with the Engineering team to productionalize high-value model outputs, tools, and data products
• Document methods, assumptions, limitations, and outputs clearly so work can be reused, reviewed, and extended by the broader analytics team

• Support basketball stakeholders with clear analysis and communication

• Collaborate with front office, coaching, scouting, player development, and basketball strategy groups to ensure analysis is connected to real basketball decisions
• Communicate complex technical findings through clear recommendations, written reports, visualizations, and presentations
• Help scope problems, prioritize work, and determine when analysis is rigorous enough to inform decisions
• Use basketball judgment to interpret model outputs, identify limitations, and translate findings into practical next steps

• Drive innovation and special projects within Basketball Analytics

• • Stay current with relevant research, modeling approaches, and basketball analytics methods
  • Identify opportunities to bring new ideas, methods, and data sources into the organization
  • Use unique basketball data and internal context to create models and insights that are difficult to replicate externally
  • Other duties as assigned

Growth Areas

• Basketball decision-support models

• Develop and refine models that help the organization evaluate players, teams, lineups, tactics, and strategic basketball questions
• Create tools that make model outputs easier to interpret, compare, and apply in basketball contexts
• Improve the way uncertainty, sample size, role, context, and fit are incorporated into analysis

• Applied research and model validation

• Strengthen research standards for testing, validation, backtesting, documentation, and reproducibility
• Explore new modeling approaches and determine when they can improve existing workflows
• Translate research into practical outputs that can be used by basketball stakeholders

• Internal tools and operational workflows

• • Build and improve tools, reports, and dashboards that help stakeholders answer recurring basketball questions
  • Partner with Engineering to move high-value prototypes into more durable, scalable products
  • Create reusable workflows that improve the speed, consistency, and quality of analytics work

People and Services

• Integrated working with Basketball Analytics leadership and team members
• Partnership with Engineering on data products, internal tools, and model deployment workflows
• Support for front office, coaching, scouting, player development, and basketball strategy stakeholders through timely, decision-oriented analysis.
• Cross-functional collaboration to ensure technical work is grounded in basketball context and connected to organizational priorities

What You'll Bring

• Basketball Curiosity and Judgment - A strong desire to understand the game, ask better questions, and connect analysis to basketball decision-making
• Technical Rigor - Strong statistical, machine learning, and research fundamentals with the ability to validate work and communicate uncertainty.
• Practical Builder - Ability to move from idea to prototype quickly, while writing clean and reproducible Python or R code
• Clear Communicator - Ability to translate complex technical work into clear, concise recommendations for technical and non-technical stakeholders.
• Ownership Mindset - Comfortable taking responsibility for ambiguous problems and driving work from question to insight
• Collaborative Teammate - Ability to work effectively across Analytics, Engineering, and basketball departments with a service-oriented approach.
• Data Science Foundation - Professional experience in data science, applied science, research science, or a similar analytical field; expertise in Python or R for data science and proficient SQL skills
• Basketball Analytics Experience - Prior sports analytics experience with a college, professional, or NBA team is a plus

The Player 15 Group is committed to employing a diverse workforce. Qualified applicants will receive consideration without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity, gender expression, veteran status, or disability.

Please note this job description is not designed to cover every activity, duty, or responsibility. Duties, responsibilities, and activities may change at any time with or without notice.

For questions about this career opportunity, please contact our People & Culture Recruiting team at [e-mail]