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Nfl Data Analytics Jobs (NOW HIRING)

Swish Analytics

Trading Analyst

San Francisco, CA · On-site +1

... analytics, betting, and fantasy startup building the next generation of predictive sports data ... Deep domain knowledge across high-turnover sporting verticals such as NBA, NFL, and Soccer

Swish Analytics

Trading Analyst

San Francisco, CA · On-site

... analytics, betting, and fantasy startup building the next generation of predictive sports data ... Deep domain knowledge across high-turnover sporting verticals such as NBA, NFL, and Soccer

Lee Enterprises

Buffalo Bills Beat Reporter

Buffalo, NY · On-site

$23.25 - $31.50/hr

Monitor data analytics to understand what our readers want. * Be a strong storyteller who can report on the inner workings of the Bills organization and the rest of the NFL. Other requirements ...

Indianapolis Colts

Data Engineer

Indianapolis, IN · On-site

$109K - $131K/yr

... NFL and third-party sources. • Support the integration of data into the enterprise data warehouse (Snowflake and Microsoft Azure). • Collaborate with CRM, Marketing, and Analytics teams to ...

indianapoliscolts

Data Engineer

Indiana, PA · On-site

$104K - $125K/yr

... NFL and third-party sources. · Support the integration of data into the enterprise data warehouse (Snowflake and Microsoft Azure). · Collaborate with CRM, Marketing, and Analytics teams to ...

Colts, Inc.

Data Engineer

Indianapolis, IN · On-site

$100 - $125/hr

... from NFL and third-party sources. * Support the integration of data into the enterprise data warehouse (Snowflake and Microsoft Azure). * Collaborate with CRM, Marketing, and Analytics teams to ...

AEG

Data Engineer

Indianapolis, IN · On-site

$109K - $131K/yr

... NFL and third-party sources. • Support the integration of data into the enterprise data warehouse (Snowflake and Microsoft Azure). • Collaborate with CRM, Marketing, and Analytics teams to ...

Fanatics Betting & Gaming

Data Scientist I

Denver, NY · On-site

... NFL stadium at Northwest Stadium. Fanatics Betting and Gaming is headquartered in New York with ... Supporting commercial and analytics stakeholders with high level Sportsbook & Casino strategy will ...

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NFL Data Analytics information

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How much do nfl data analytics jobs pay per hour?

As of Sep 8, 2026, the average hourly pay for nfl data analytics in the United States is $59.24, according to ZipRecruiter salary data. Most workers in this role earn between $58.65 and $59.86 per hour, depending on experience, location, and employer.

What is an NFL Data Analytics?

An NFL Data Analytics job involves collecting, interpreting, and visualizing football-related data to support teams, coaches, and analysts in making informed decisions. Professionals in this role use statistical models and machine learning techniques to evaluate player performance, game strategies, and scouting insights. They work with large datasets, including player tracking data and in-game statistics, to optimize team performance, prevent injuries, and gain a competitive edge. These roles can be found within NFL teams, media companies, or technology firms specializing in sports analytics. Strong skills in programming, data analysis, and football knowledge are essential for success in this field.

What are the typical responsibilities of an NFL Data Analytics professional during the football season?

During the football season, NFL Data Analytics professionals are responsible for collecting and analyzing player and team performance data, preparing reports for coaches, and generating predictive models for upcoming games. They often collaborate with coaching staff and scouts to translate data insights into actionable game plans, while also monitoring player health and workload metrics. The role may also involve real-time analysis during games to support strategy adjustments. Being able to communicate complex analyses clearly to non-technical stakeholders is a frequent and critical task.

What are the key skills and qualifications needed to thrive in the NFL Data Analytics position, and why are they important?

To thrive in NFL Data Analytics, you need strong skills in statistics, data modeling, programming (such as Python or R), and a relevant degree in mathematics, statistics, computer science, or a related field. Familiarity with data visualization tools, advanced analytics platforms, and sports-specific databases is highly valued, with certifications in data analytics being a plus. Excellent attention to detail, collaboration, and communication skills are essential for presenting insights to coaches and executives. These competencies ensure data-driven decision-making that directly impacts team performance and strategy.

Do NFL teams hire data analysts?

Yes, NFL teams often hire data analysts to evaluate player performance, develop strategies, and improve decision-making using statistical tools and data analysis techniques. These roles typically require skills in data management, programming, and sports analytics software, and may involve working closely with coaching staff and management.

How to become an NFL Data Analytics analyst?

To become an NFL Data Analytics analyst, you typically need a bachelor's degree in fields like statistics, data science, or sports management. Developing skills in programming languages such as Python or R, and proficiency with data visualization tools like Tableau, are also important. Gaining experience through internships or projects related to sports analytics can improve job prospects.
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NFL Data Analytics jobs near you
Infographic showing various Nfl Data Analytics job openings in the United States as of September 2026, with employment types broken down into 87% Full Time, 9% Part Time, and 4% Temporary. Highlights an 83% In-person, and 17% Remote job distribution, with an average salary of $123,210 per year, or $59.2 per hour.

Trading Analyst

San Francisco, CA • On-site, Remote

Swish Analytics
Spectator Sports • 1 - 10 employees
Learn more about Swish Analyticsexternal

Full-time

Re-posted 13 days ago

Job description

Company Overview
Swish Analytics is a sports analytics, betting, and fantasy startup building the next generation of predictive sports data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports expertise, not intuition. We are looking for team-oriented individuals with an authentic passion for accurate, predictive, real-time data who can execute in a fast-paced, creative, and continually evolving environment without sacrificing technical excellence.
Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building high-performance pricing and trading systems.
Job Description
Swish is looking for a highly analytical Sports Trading Analyst to help strengthen and scale our sports pricing and trading operation.
In this role, you will work at the intersection of sports intelligence, pricing strategy, and live market behaviour. You will help manage and improve real-time pricing across a range of sports and market types, with a particular focus on market aware price discovery, risk management and the identification of actionable trading signals from market activity.
This role is suited to someone with strong quantitative reasoning, excellent decision-making under pressure, and a deep interest in how markets are formed, odds move, and how to engineer accurate pricing in the competitive sports betting environment.
You will work in a geographically dispersed team alongside experienced traders, quants, data scientists, and engineers, with colleagues based across Europe and the US.
Duties
  • Monitor live sports markets and market activity in real time across a range of sports and market types
  • Support the calibration and refinement of prices using market signals, statistical models, competitor benchmarking, and event-driven information
  • Help improve pricing quality through the analysis of market behaviour, price sensitivity, liquidity patterns, and reaction speed to new information
  • Contribute to the development, testing, and refinement of quantitative models by applying your understanding of live market dynamics and pricing behaviour
  • Own and manage real-time trading risk, including exposure monitoring, liability controls, and disciplined decision-making across concurrent events
  • Collaborate with engineering on trading and pricing infrastructure, including API integrations, automated monitoring, alerting, anomaly detection, and execution tooling
  • Work closely with Sports Trading teams to interpret breaking news, lineups, injuries, team news, and other event-specific developments to ensure timely and accurate price updates
  • Identify model discrepancies, edge cases, and structural inefficiencies in pricing workflows, escalating and documenting findings for Data Science and Data Engineering teams
  • Help evaluate market opportunities, prioritise resources across sports and competitions, and improve operational processes as the trading function scales
  • Detect sharp or informative market activity and ensure useful signals are fed back into Swish's proprietary models and pricing systems
  • Communicate effectively with internal Sports Trading teams responsible for maintaining and improving our core sportsbook pricing models
Requirements
  • Bachelor's degree or higher in a quantitative or analytical discipline (Mathematics, Statistics, Computer Science, Economics, Engineering, Quantitative Finance, or similar), or equivalent practical experience
  • Must have demonstrated professional experience in exchange-style environments, sports trading, sports betting, market-making, quantitative trading, or other closely related domains where fast price formation and disciplined execution matter
  • Strong grounding in probability, statistics, and expected value, with the ability to reason clearly about fair price, uncertainty, and risk
  • Strong understanding of sports betting fundamentals, including odds formats (decimal, fractional, American), implied probability conversion, expected value, and closing line value
  • Demonstrated ability to make high-quality decisions under time pressure with incomplete information during live events
  • Comfortable working autonomously across global event schedules, including weekends and major tournament periods
  • Fluent in English, written and spoken, with clear communication skills in a distributed and asynchronous team environment
Preferred (but not essential)
  • Track record of building and backtesting quantitative models using real historical data; GitHub, notebooks, or demonstrable analytical work is highly valued
  • Deep domain knowledge across high-turnover sporting verticals such as NBA, NFL, and Soccer
  • Understanding of relational database systems (MySQL or equivalent) for analysis of prices, outcomes, and trading decisions
  • Familiarity with market microstructure concepts such as adverse selection, inventory risk, liquidity dynamics, queue positioning, or execution quality
  • Experience using Python for quantitative research, exploratory data analysis, prototyping, or model improvement
  • Experience using modern AI tools to accelerate analysis, research, and modelling workflows
Why Join
This is an opportunity to play a meaningful role in a growing and well-resourced sports trading operation. The successful candidate will help shape process, tooling, and decision-making within a team focused on high-quality pricing, efficient execution, and long-term product excellence across multiple sports verticals.
Swish Analytics is an Equal Opportunity Employer. All candidates who meet the qualifications will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, pregnancy status, genetic, military, veteran status, marital status, or any other characteristic protected by law. The position responsibilities are not limited to the responsibilities outlined above and are subject to change. At the employer's discretion, this position may require successful completion of background and reference checks.
Department Trading Operations Locations San Francisco, CA - Remote, Malta - Remote, Spain - Remote, United Kingdom - Remote Remote status Fully Remote

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