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About This Role
We are seeking a highly motivated and experienced Sr. Manager of Club Revenue Analytics & Data Science to join our Data & Analytics team at the NFL. Based in the League's New York office, this role partners with internal stakeholders and all 32 clubs to turn business questions and fan data into actionable recommendations, scalable analytics products, and measurable growth across ticketing, premium seating, sponsorship, and fan engagement. The role also works with Data Engineering and Business Intelligence to ensure solutions are accurate, governed, and scalable.
The ideal candidate combines business acumen, data science and advanced analytics experience, executive communication skills, and a consultative mindset to turn insights into action.
Responsibilities
- Manage and collaborate with stakeholders from Club Business Development and the 32 clubs to understand their analytics needs and deliver actionable insights.
- Oversee the development and ongoing maintenance of comprehensive analytics platforms to support ticketing, premium seating and sponsorship analytics and insights.
- Lead advanced analytics to evaluate ticketing, premium seating, and sponsorship behavior, including KPI monitoring, segmentation, customer profiling, and predictive modeling.
- Effectively communicate analytical findings to stakeholders, translating complex data analysis into clear and concise insights.
- Collaborate with BI and technology teams to ensure ticketing, premium seating, and sponsorship platforms receive accurate, reliable data through regular quality audits and validation.
- Perform data extraction, transformation and analysis using SQL and Python.
- Develop and implement attribution models to measure the impact of different League-provided data products.
- Work with the BI COE to create dashboards to provide insights and recommendations based on analytics findings.
- Stay updated with industry trends and best practices in club analytics and data quality management.
- Collaborate with cross-functional teams, including Club Business Development, Marketing and the 32 clubs to align analytics initiatives and drive data-driven decision-making.
- Collaborate with analytics consulting vendors.
- Foster a collaborative and agile work environment, leveraging tools like Jira for project management and task tracking.
Required Qualifications
- Bachelor's degree in Data Science, Analytics, Economics, Statistics, Operations Research, Business Analytics, or a related quantitative discipline.
- At least 7 years of relevant experience in revenue analytics, pricing, customer analytics, ticketing, consulting, ecommerce, hospitality, sports, media, or another commercially oriented industry or other relevant industry experience.
- Demonstrated ability to structure ambiguous business problems, direct analytical work, and translate findings into clear commercial recommendations.
- Strong proficiency in SQL and Python, with practical experience working with large customer, transactional, or behavioral datasets.
- Experience with predictive modeling, customer segmentation, forecasting, experimentation or campaign measurement, and analytical storytelling.
- Demonstrated experience leading complex analytical workstreams and coaching analysts
- Proven ability to communicate with senior business and technical stakeholders and present complex analysis in a clear, concise, and influential manner.
- Experience partnering with data engineering, BI, product, or technology teams to deliver scalable analytical solutions with strong data quality and governance.
- 3 years of management experience.
Other Key Attributes / Characteristics
- Sports, clubs, ticketing, live events, venue, hospitality, airline, subscription, or revenue management experience.
- Experience with Ticketmaster, SeatGeek, or comparable ticketing and live-event datasets.
- Experience developing club-facing, client-facing, or executive-facing recommendations in a consulting or analytics advisory environment.
- Excellent analytical and problem-solving skills with high attention to detail.
- Strong communication skills, with the ability to convey complex analytical findings to stakeholders in a clear and concise manner.
- Strong collaborator who communicates transparently, supports shared goals, and welcomes constructive challenges.
Salary / Pay Range
This job posting contains a pay range, which represents the range of salaries or hourly rates that the NFL believes, in good faith, at the time of this posting that it might be willing to pay for the posted job in the location(s) specified. The NFL expects to hire for this position near the middle of the range. Only in truly rare and exceptional circumstances, where an external candidate has experience, credentials or expertise that far exceed those required or expected for the position, would the NFL consider paying a salary or rate near the higher end of the range.
Terms / Expected Hours of Work
- This position may require some limited travel. (>10%)
Benefits Information
To learn more about our comprehensive benefits offerings, please visit: NFL BENEFITS
At the NFL, in-person work at our offices is a top priority because it allows us to collaborate more effectively, build stronger connections, and maintain the culture that drives our success. This role requires onsite presence at an NFL office or stadium location, and remote and/or hybrid working options are not offered.
NO RELOCATION ASSISTANCE WILL BE PROVIDED.
The NFL is committed to building an inclusive work environment that reflects our incredible fan base. We provide an environment of mutual respect where equal employment opportunities are available to all employees and applicants without regard to status as protected by applicable federal, state, and local laws.
WHO WE ARE:
NFL Core Values:
- Respect: Everyone matters. We celebrate diverse opinions, honor hard work, and value every contribution.
- Integrity: We do what's right, even when it's tough. We hold ourselves accountable and always follow through.
- Team Responsibility: We support each other and our communities. No one is bigger than the game, and every action impacts others.
- Resiliency: We set high standards, overcome adversity, and adapt to challenges, always striving for excellence.
NFL Leadership Attributes:
- Build Talent: We develop and nurture potential, empowering individuals to grow and succeed.
- Execute: We take action with precision, delivering results that drive our goals forward.
- Inspire: We motivate others through vision, energy, and a commitment to excellence.
- Live Our Values: We embody our core principles in every decision and action.
- Know the Business: We stay informed, understand our industry, and make decisions that strengthen our position.
- Think Big: We challenge the status quo, envision bold possibilities, and strive for transformative impact
WHO YOU ARE:
Talent Attributes: What we expect for our employees:
- Embody an enthusiastic, proactive can-do attitude
- Embrace grit, free from ego or entitlement
- Excel as a relationship builder, with the ability to influence
- Eager learner, driven by passion rather than just ambition
- Encompasses an incredible work ethic with an agile mindset
Salary Context
This $130K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At National Football League, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($155K) sits 27% below the category median. Disclosed range: $130K to $180K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
National Football League AI Hiring
National Football League has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $180K - $180K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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