Home › Companies › Careers Mlssoccer Icims Com › Senior Data Scientist
Senior Data Scientist
Careers Mlssoccer Icims Com · New York, NY, US · On Site · Active · $130,000–$150,000 / day · iCIMS
Job facts
| Field | Value |
|---|---|
| Company | Careers Mlssoccer Icims Com |
| Title | Senior Data Scientist |
| Normalized title | - |
| Department / team | Strategy and Business Intelligence |
| Location | New York, NY, United States |
| Work model | On Site |
| Employment type | OTHER |
| Salary | $130,000–$150,000 / day |
| Status | active |
| ATS provider | iCIMS |
| Posted / first seen | 2026-02-12 / 2026-05-31 |
| Changed / last seen | 2026-06-01 / 2026-06-06 |
Related slices
| Page | What it contains | Open |
|---|---|---|
| Company jobs | Active postings from Careers Mlssoccer Icims Com. | Open |
| Company breakdowns | Role, location, ATS, and work model facets for this company. | Open |
| ATS provider jobs | Active postings observed through iCIMS. | Open |
| Provider filtered search | The same provider as a filtered job collection. | Open |
| City jobs | Active postings in New York. | Open |
| Department jobs | Active postings in Strategy and Business Intelligence. | Open |
| Work model jobs | Active On Site postings. | Open |
| Lifecycle events | Open, update, close, and reopen events for this posting. | Open |
| Original posting | Canonical source or apply URL captured from the ATS. | Open |
Linked records
| Company | Careers Mlssoccer Icims Com |
| Source | e94212a1-e96a-43f1-b519-0f0c6640b2c0 |
| ATS provider | iCIMS |
Description
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.
Full job record
| Job ID | 1e03b69fb538d2ba6f614aa2ea41761c3572ed9c |
| Org ID | f763579b-24c5-4e47-a76d-b2d2106538f0 |
| Source ID | e94212a1-e96a-43f1-b519-0f0c6640b2c0 |
| Board ID | e94212a1-e96a-43f1-b519-0f0c6640b2c0 |
| Provider | icims |
| Provider Job Key | 2237 |
| Title | Senior Data Scientist |
| Normalized Title | — |
| Status | active |
| Active | yes |
| Location Text | New York, NY, US |
| Department | Strategy and Business Intelligence |
| Team | — |
| Employment Type | OTHER |
| Workplace Type | on_site |
| Remote Policy | — |
| Country | United States |
| Region | NY |
| City | New York |
| Salary Raw | 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. |
| Salary Min | 130,000 |
| Salary Max | 150,000 |
| Salary Currency | USD |
| Salary Period | day |
| Source URL | https://careers-mlssoccer.icims.com/jobs/2237/senior-data-scientist/job |
| Apply URL | https://careers-mlssoccer.icims.com/jobs/2237/senior-data-scientist/job |
| First Seen At | 2026-05-31 18:49:05Z |
| Last Seen At | 2026-06-06 08:38:07Z |
| Last Checked At | 2026-06-06 08:38:07Z |
| Last Changed At | 2026-06-01 14:04:19Z |
| Inactive At | — |
| Source Posted At | 2026-02-12 05:00:00Z |
| Source Updated At | 2026-05-04 13:54:03Z |
| Raw Payload Uri | s3://job-postings-prod-raw-590183727216/raw/provider=icims/board=careers-mlssoccer.icims.com/date=2026-06-06/2026-06-06T08-38-06-386Z-89f975f0c165e26037658cc4d30a6e9d0ac6bd9398486069291e2ed63ae527d8.json |
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"description": "<h2>Overview</h2>\n<p>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. </p>\n<p>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. </p>\n<p>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. 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While dashboarding and reporting may be part of the work, the core emphasis is on building and delivering data-driven models. </li>\n <li>Real-world impact: Your models will directly influence fan engagement, marketing strategy, and revenue outcomes </li>\n <li>High ownership: You will take over key modeling areas and evolve them beyond current vendor-supported solutions </li>\n <li>Fast-paced environment: You will operate in a high-expectation environment with meaningful deadlines and visibility </li>\n</ul>\n<p> </p>\n<h2>Qualifications</h2>\n<ul>\n <li>Bachelor’s Degree required </li>\n <li>8+ years of experience required </li>\n</ul>\n<p><strong>Total Rewards</strong><strong> </strong> </p>\n<p>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. 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