Overview
Major League Soccer’s Strategy and Business Intelligence group is tasked with supporting strategic decision making and resource allocation across the League – with a focus on driving fan growth, revenue capture, and operating efficiencies – by providing impactful data driven insights, analysis, and recommendations.
We are looking for a Senior Data Scientist to build and own advanced machine learning models that drive fan growth, engagement, and revenue across the League.
This is a highly technical, hands-on role where you will spend the majority of your time working in code, developing models, and solving complex data problems. You will work with large-scale fan and marketing datasets to power segmentation, personalization, and predictive insights that directly influence business strategy.
If you are someone who enjoys going deep on data, building models end-to-end, and seeing your work deployed into real-world applications, this role offers a unique opportunity to have visible impact at scale.
Responsibilities
- Build and deploy machine learning models across segmentation, forecasting, recommendation, and classification use cases
- Own the full model lifecycle including data exploration, feature engineering, training, validation, production deployment and impact analysis
- Develop customer segmentation and clustering models to optimize fan growth and engagement
- Design and implement personalization and “next best action” models
- Lead modeling efforts for media mix and marketing performance optimization
- Partner with Data Engineering to productionize models and integrate outputs into downstream systems
- Work closely with internal stakeholders to translate model outputs into actionable insights
Required Skills and Experience
- Strong hands-on experience building and deploying machine learning models in Python
- Deep expertise in SQL and processing large-scale datasets in cloud environments, including distributed compute with Spark/PySpark (EMR) and developing robust, scalable batch data pipelines
- Experience with core Python data and ML libraries, including NumPy, pandas, and scikitlearn
- Experience applying a range of machine learning techniques, including regression, decision trees and ensemble methods (e.g., XGBoost, LightGBM), clustering, causal inference, and recommendation systems
- Experience with ML/data engineering infrastructure and distributed processing frameworks
- (e.g., Airflow, AWS SageMaker, EMR, PySpark) to build, orchestrate, and scale end-to-end ML pipelines
- Experience with experimental design and measurement, including hypothesis testing, A/B and multivariate testing, and causal inference, with the ability to design, analyze, and interpret controlled experiments
- Experience with applied AI systems, including developing and deploying agentic workflows, applying techniques such as retrieval-augmented generation (RAG), fine-tuning, and prompt engineering
- Experience contributing to the design of conversational and semantic layers to ensure accurate, reliable outputs • Strong communication and collaboration skills.
- Strong interpersonal skills and the ability to effectively communicate, both verbally and in writing
- Demonstrated decision making and problem-solving skills.
- High attention to detail with the ability to multi-task and meet deadlines with minimal supervision
- Proficiency in Word, Excel, PowerPoint and Outlook.
What Makes This Role Different
- Builder-first role: This role is primarily focused on coding, modeling, and deploying solutions. While dashboarding and reporting may be part of the work, the core emphasis is on building and delivering data-driven models.
- Real-world impact: Your models will directly influence fan engagement, marketing strategy, and revenue outcomes
- High ownership: You will take over key modeling areas and evolve them beyond current vendor-supported solutions
- Fast-paced environment: You will operate in a high-expectation environment with meaningful deadlines and visibility
Qualifications
- Bachelor’s Degree required
- 8+ years of experience required
Total Rewards
Major League Soccer offers a competitive starting base salary of $130,000 - $150,000, based on individual qualifications, market financials, and operational business needs. We are committed to providing a Total Rewards package that attracts, supports, engages, and retains talent. Our benefits package includes comprehensive medical, dental, and vision coverage, a $500 wellness reimbursement, and generous Holiday and PTO schedule to promote work-life balance. We also prioritize career and professional development, offering on-the-job training, feedback, and ongoing educational opportunities.
Major League Soccer believes in the value of in-person collaboration to support teamwork, creativity, and connection. Employees in this role are expected to work a four (4) day in-office schedule, with the flexibility to work remotely one (1) day each week, based on business and department needs.
Major League Soccer is an equal opportunity employer. Employment decisions are made without regard to race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, disability, genetic information, protected veteran status, or any other characteristic protected by applicable law.
Major League Soccer is committed to providing reasonable accommodations to individuals with disabilities throughout the application and hiring process, as well as during employment. Applicants who require an accommodation may contact Human Resources to request assistance.
Join our team and help support the growth and success of Major League Soccer.