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About This Role
EverPass Media is seeking an experienced Sr. Manager, Data, Analytics \& AI to lead the company's enterprise data strategy while building the next generation of AI powered capabilities that drive growth, operational excellence, and product innovation.
This leader will own the end\-to\-end data ecosystem, from infrastructure and business intelligence to executive reporting, governance, advanced analytics, and enterprise AI strategy. This individual will partner closely with Product, Engineering, Sales, Marketing, Finance, Customer Success, and Operations to ensure data is trusted, accessible, and actionable while identifying opportunities to leverage machine learning, predictive analytics, and Generative AI across the organization. This is a highly visible leadership role requiring equal strengths in technology, business strategy, and organizational influence.
Key Responsibilities
- Enterprise Data Strategy
- Own the enterprise data strategy and multi\-year roadmap.
- Define the company's data architecture and technology strategy.
- Lead evolution of the modern data platform, including data warehouse, integrations, pipelines, and governance.
- Establish data quality standards and a trusted single source of truth across the business.
- Prioritize investments based on strategic business impact.
Data Infrastructure \& Engineering
- Own the end\-to\-end data platform.
- Oversee ETL/ELT pipelines and cloud data infrastructure.
- Ensure scalability, security, reliability, and governance.
- Partner with Engineering to support product data needs.
- Evaluate and implement best\-in\-class data technologies.
Business Intelligence \& Analytics
- Own enterprise reporting strategy.
- Develop executive dashboards and KPI reporting.
- Enable self\-service analytics across the organization.
- Lead deep analytical projects supporting Product, Revenue, Marketing, Finance, Customer Success, and Operations.
- Build executive reporting for leadership, Board presentations, and strategic planning.
Artificial Intelligence Strategy
- Develop EverPass's enterprise AI roadmap.
- Identify high\-value AI opportunities across every business function.
- Partner with Product and Engineering to embed AI capabilities into customer\-facing products.
- Lead implementation of predictive analytics and machine learning initiatives.
- Evaluate and implement Generative AI solutions that improve internal productivity and decision\-making.
- Drive AI adoption across the business through education, governance, and change management.
- Build responsible AI standards covering privacy, security, model governance, and monitoring.
- Evaluate emerging AI technologies and make build\-versus\-buy recommendations.
Data Governance
- Define company\-wide KPI definitions.
- Maintain enterprise data catalog and data dictionary.
- Establish governance policies and ownership across business functions.
- Improve consistency and trust in reporting.
Leadership
- Build and scale a high\-performing Data \& AI organization.
- Mentor engineers, analysts, and data scientists as the team grows.
- Serve as the executive thought partner on analytics, AI, and business intelligence.
- Foster a culture of experimentation, innovation, and data\-driven decision making.
Qualifications
- 7\+ years of experience in Data, Analytics, Data Engineering, or AI.
- 3\+ years leading technical teams.
- Strong SQL expertise.
- Experience with Snowflake, BigQuery, Redshift, or similar platforms.
- Experience building enterprise BI environments.
- Experience with Tableau, Power BI, Looker, or equivalent.
- Experience with Salesforce data architecture.
- Experience implementing AI, machine learning, or Generative AI solutions in production.
- Familiarity with Python and modern AI frameworks.
- Strong executive communication skills.
- Experience operating in fast\-paced technology organizations.
Preferred
- Experience within media, streaming, SaaS, or sports technology.
- Experience building enterprise AI programs.
- Experience with LLMs and Retrieval\-Augmented Generation (RAG).
- Experience with Azure OpenAI, OpenAI, Anthropic, AWS Bedrock, or Google Vertex AI.
- Experience with dbt, Fivetran, Airbyte, or modern ELT tooling.
- Experience building Data and AI organizations from early growth through scale.
Success in the First 12 Months
- Establish a trusted enterprise data platform.
- Deliver executive KPI dashboards.
- Launch a company\-wide data governance framework.
- Identify and deliver multiple AI use cases with measurable business impact.
- Build a scalable roadmap for enterprise analytics and AI.
- Improve organizational data literacy and adoption.
Expected Compensation: The anticipated range for a new hire into this position is $160,000 \- $200,000\. In compliance with local law, the range above reflects the current hiring range for this position. EverPass takes into consideration the qualifications, skills, and experience of the candidate, expected quality and quantity of work, and internal pay alignment when determining the salary level for potential new employees. EverPass expects to hire for this position at the mid\-range salary, with the possibility of considering a higher salary only in rare cases when EverPass determines an external candidate possesses exceptional qualifications significantly exceeding the job requirements.
About Us:
EverPass Media is a comprehensive media platform dedicated to commercial businesses, that aggregates, distributes and enables streaming of live sports and entertainment content, and offers a wide array of consumer engagement and performance marketing tools for bars, restaurants, hotels and other commercial venues. Launched initially as the exclusive distributor of NFL Sunday Ticket to commercial establishments in the United States, EverPass partners with rightsholders, distribution partners and business owners to unlock greater access to premium live events and drive business growth. EverPass was founded in 2023 in partnership with RedBird Capital Partners and 32 Equity, the strategic investment arm of the National Football League. TKO Group Holdings, parent company of UFC and WWE, joined as an investor in 2024\.
Benefits and Perks:
- Competitive Compensation
- Medical, dental, vision, life, and long\-term and short\-term disability insurance
- Professional Development Programs
- Access to senior management and mentoring opportunities
- Employee Recognition Program
- Paid Parental Leave
- Mental Health and Recharge Days
- 401k Match
- Pre\-tax Transportation
- Employee Assistance Program
- In\-person and Virtual Social Events including: Team Wins and Highlights from the Month, Cultural and Diversity Spotlights, and Happy Hours
This is a chance for you to join a challenging and inspiring environment where you will have the possibility to make a daily impact. Every day you will work alongside helpful and down\-to\-earth colleagues who are dedicated and ambitious. Together we create an innovative environment that drives EverPass forward. If you are the right person for the role you will be part of a fantastic journey in a dynamic, high\-growth business. We look forward to your application.
EverPass is an equal opportunity workplace and an affirmative\-action employer. We are always committed to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Discrimination is not welcome on the basis of any other status protected by the laws or regulations in the locations where we work.
Salary Context
This $160K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At EverPass Media, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($180K) sits 18% below the category median. Disclosed range: $160K to $200K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
EverPass Media AI Hiring
EverPass Media has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Greenwich, CT, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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