Data Scientist, Business Ventures

UNITED STATES SOCCER FEDERATION 1•Atlanta, GA

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About The Position

U.S. Soccer is seeking a Data Scientist to help transform data into actionable insights, intelligent products, and measurable business outcomes. The Data Scientist will join the Business Ventures team, advancing data-driven decision-making across the Federation, working with stakeholders throughout Commercial, Marketing, Ticketing, Membership, E-commerce, Advancement, Product, and Technology to identify where analytics, machine learning, experimentation, and artificial intelligence can improve performance and accelerate growth. This is not a reporting role. The Data Scientist will take ownership of complex business problems, translate them into analytical frameworks, and work with partners to put those solutions into practice. Projects may include understanding what drives soccer fandom in the United States, forecasting demand for matches and products, predicting fan behavior, improving audience segmentation, measuring campaign effectiveness, developing customer intelligence, and building AI-powered tools that make data more accessible across the organization. The ideal candidate combines strong technical capabilities with curiosity, business judgment, and communication skills. They should be comfortable working with large and sometimes imperfect datasets, able to explain complex findings to nontechnical audiences, and genuinely interested in what creates and sustains a soccer fan—why someone attends a match, tunes in, buys a jersey, or brings a child into the game.

Requirements

Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, Operations Research, or another quantitative field, or equivalent professional experience.
Three or more years of professional experience in data science, advanced analytics, machine learning, or a closely related role.
Strong proficiency in SQL, including joins, aggregations, and window functions, with comfort querying large tables across multiple schemas.
Working knowledge of relational databases and data warehousing, with hands-on experience in a cloud data warehouse such as Amazon Redshift, Snowflake, BigQuery, or Databricks. This role works in Amazon Redshift daily.
Strong proficiency in Python and/or R and commonly used data-science libraries such as pandas, NumPy, scikit-learn, and XGBoost.
Experience using Git and GitHub for version control, pull requests, and code review.
Demonstrated experience developing and evaluating statistical or machine-learning models, with a strong understanding of model validation, feature engineering, performance metrics, bias, overfitting, and model limitations.
Experience translating business problems into analytical approaches and actionable recommendations.
Ability to communicate technical concepts clearly to stakeholders with varying levels of analytical experience.
Strong attention to detail and a commitment to producing accurate, reproducible, and well-documented work.
Proven ability to manage projects independently, prioritize competing needs, and deliver results in a fast-paced and evolving environment.
Team-first attitude with strong interpersonal and collaboration skills, and the ability to connect micro-level details to the organization's macro vision and mission.

Nice To Haves

Master's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Business Analytics, or another quantitative discipline.
Experience deploying and monitoring machine-learning models in a production environment.
Experience with AWS services such as S3, Lambda, Bedrock, and EC2, including provisioning and maintaining cloud compute instances.
Experience building and deploying analytical web applications end to end—standing up a site on EC2 or similar, configuring a domain, and keeping it running for internal users.
Experience with continuous deployment workflows such as GitHub Actions, along with automated testing and code review practices.
Experience with performance tuning and query optimization in Redshift or a comparable cloud data warehouse.
Experience with customer, marketing, ticketing, e-commerce, membership, CRM, or digital engagement data, and familiarity with platforms such as Salesforce, Braze, Ticketmaster, Stripe, or Shopify.
Experience developing generative AI applications, natural-language data tools, retrieval-augmented generation systems, or AI agents.
Experience developing dashboards or analytical applications using tools such as Streamlit, Tableau, Power BI, or similar platforms.
Experience within sports, entertainment, media, membership organizations, or another consumer-focused industry, including an understanding of how teams, leagues, federations, broadcasters, and sponsors generate revenue and compete for audience attention.

Responsibilities

Develop, validate, and deploy predictive models supporting fan engagement, ticket sales, membership growth, retention, e-commerce, fundraising, and marketing—including propensity, classification, forecasting, segmentation, recommendation, and customer-value models.
Develop a deep understanding of what drives soccer fandom in the United States: how fans discover the sport, what converts casual interest into attendance, viewership, and purchase, and how those drivers differ across markets, teams, and competitions.
Forecast demand for matches, broadcasts, and products, and isolate the primary drivers behind changes in attendance, viewership, and revenue so stakeholders understand not just what is projected, but why.
Select appropriate statistical and machine-learning methods based on the business problem, available data, and intended use of the results.
Establish model evaluation, monitoring, documentation, and regression-testing processes so solutions remain reliable over time.
Develop audience segments and analytical frameworks that improve personalization, targeting, acquisition, retention, and lifetime value, and evaluate customer journeys to deepen relationships with current supporters and reach new soccer audiences.
Partner with Marketing, CRM, and other stakeholders to embed model outputs into campaigns, platforms, and lifecycle strategies.
Identify opportunities to responsibly apply generative AI, natural-language interfaces, and intelligent automation, and contribute to AI-powered tools that help employees answer their own questions.
Work directly with stakeholders to translate broad or ambiguous business questions into clearly defined analytical problems.
Present findings and recommendations clearly and compellingly to both technical and nontechnical audiences, and create visualizations and decision-support materials that make complex analysis actionable.
Build strong relationships across departments and serve as a trusted analytical partner.
Balance multiple projects and priorities while maintaining high standards for accuracy, transparency, and execution.