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Remote Football Data Analytics Jobs (NOW HIRING)

Serotonin

Blockchain, Data Analytics (Remote)

New York, NY · On-site +1

$80K - $150K/yr

Build and maintain data pipelines that power both ad-hoc analyses and production dashboards ... Remote Work Environment * Maternity/Paternity Leave $80,000 - $150,000 a year The salary range for ...

Elder Research

Data Analyst (Remote)

Four (4) years relevant experience in applied research, big data analytics, statistics, applied mathematics, data science, computer science, operations research or other closely related other ...

Elder Research

Data Analyst (Remote)

Four (4) years relevant experience in applied research, big data analytics, statistics, applied mathematics, data science, computer science, operations research or other closely related other ...

Erie Insurance

Senior Data & Analytics Specialist

Erie, PA · On-site +1

$70K - $112K/yr

... to Erie's remote work bank. * The hiring team will consider candidates for Data & Analytics ... Specialist II, or Senior Data & Analytics Specialist level position. The level of position offer ...

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Remote Football Data Analytics information

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$94

How much do remote football data analytics jobs pay per hour?

As of Jul 21, 2026, the average hourly pay for remote football data analytics in the United States is $54.75, according to ZipRecruiter salary data. Most workers in this role earn between $43.99 and $62.02 per hour, depending on experience, location, and employer.

How do remote football data analytics professionals typically collaborate with coaches and team analysts?

Remote football data analytics professionals often work closely with coaches and on-site analysts through regular virtual meetings, data-sharing platforms, and real-time communication tools. They are responsible for delivering insightful reports, visualizations, and actionable recommendations that inform game strategies and player development. Clear communication and a collaborative mindset are essential, as these professionals need to translate complex data into accessible insights while responding promptly to feedback and changing team needs.

What is the difference between Remote Football Data Analytics vs Remote Sports Data Analyst?

AspectRemote Football Data AnalyticsRemote Sports Data Analyst
CredentialsDegree in Data Science, Sports Management, or related field; knowledge of football statisticsDegree in Data Science, Sports Management, or related field; broad sports knowledge
Work EnvironmentRemote, focused on football data projectsRemote, covering multiple sports or general sports data analysis
Industry UsagePrimarily in football clubs, leagues, and football-focused mediaIn various sports organizations, media outlets, and analytics firms
Search & Comparison IntentSpecific to football data analysis rolesBroader sports data analysis roles

Remote Football Data Analytics focuses specifically on football-related data, requiring specialized football knowledge and analytics skills. In contrast, Remote Sports Data Analyst roles encompass multiple sports, demanding broader sports industry understanding. Both roles often share similar credentials and work environments but differ in their scope and industry focus.

What is a Remote Football Data Analyst?

A Remote Football Data Analyst is a professional who collects, processes, and interprets football-related data from a remote location, often using specialized software and statistical methods. They analyze match statistics, player performance data, and tactical trends to provide insights for teams, media, or betting companies. Their work helps improve team strategies, scouting, and fan engagement, all while working outside of a traditional office or stadium environment. This role requires strong analytical skills, a deep understanding of football, and proficiency in data analysis tools.

What are the key skills and qualifications needed to thrive as a Remote Football Data Analyst, and why are they important?

To thrive as a Remote Football Data Analyst, you need strong skills in statistical analysis, data interpretation, and a solid understanding of football tactics, typically supported by a degree in statistics, mathematics, or sports science. Proficiency with data analysis tools like Python, R, SQL, and sports analytics platforms such as Opta or Wyscout is highly valued. Excellent communication, attention to detail, and the ability to work independently are crucial soft skills for this remote role. These skills are important to deliver actionable insights that drive team performance and informed decision-making in a competitive sports environment.
More about Remote Football Data Analytics jobs
What cities are hiring for Remote Football Data Analytics jobs? Cities with the most Remote Football Data Analytics job openings:
What are the most commonly searched types of Football Data Analytics jobs? The most popular types of Football Data Analytics jobs are:
What states have the most Remote Football Data Analytics jobs? States with the most job openings for Remote Football Data Analytics jobs include:
What job categories do people searching Remote Football Data Analytics jobs look for? The top searched job categories for Remote Football Data Analytics jobs are:
Remote Football Data Analytics jobs near you
Infographic showing various Remote Football Data Analytics job openings in the United States as of July 2026, with employment types broken down into 1% Internship, 94% Full Time, 3% Part Time, and 2% Contract. Highlights an 79% Physical, 5% Hybrid, and 16% Remote job distribution, with an average salary of $113,873 per year, or $54.7 per hour.
Data Analytics Developer (Remote Eligible)

Data Analytics Developer (Remote Eligible)

Mathematica

Washington, DC • On-site, Remote

Other

Re-posted 11 days ago

Job description

Mathematica applies expertise at the intersection of data, methods, policy, and practice to improve well-being around the world. We collaborate closely with public- and private-sector partners to translate big questions into deep insights that improve programs, refine strategies, and enhance understanding. Our work yields actionable information to guide decisions in wide-ranging policy areas, from health, education, early childhood, and family support to nutrition, employment, disability, and international development. Mathematica offers our employees competitive salaries, and a comprehensive benefits package, as well as the advantages of being 100 percent employee owned. As an employee stock owner, you will experience financial benefits of ESOP holdings that have increased in tandem with the company's growth and financial strength. You will also be part of an independent, employee-owned firm that is able to define and further our mission, enhance our quality and accountability, and steadily grow our financial strength. Learn more about our benefits here: https://www.mathematica.org/career-opportunities/benefits-at-a-glance

At Mathematica, we take pride in our commitment to diversity. Building an inclusive culture that draws on the individual strengths of employees from different ethnic backgrounds, cultures, lifestyles, abilities, and experience is key to our success.

We are seeking an intellectually curious Data Analytics Developer in any of our locations who is passionate about using big data to answer research questions that influence decision making in U.S. health care policy. The ideal candidate will work in languages such as Python and SQL. Individuals with an interest in confronting the challenges of working with large, complex data sets, an interest in data manipulation and analysis, and a desire to become a subject matter expert in health care data are strongly encouraged to apply.

Responsibilities:

  • Working with large secondary data sources such as Medicare and Medicaid administrative claims data and survey data to effectively answer research questions about health care policy
  • Providing creating and best practice solutions for data processing optimization, commonly on cloud computing platforms
  • Ensuring data quality through all stages of data processing with rigorous quality control
  • Iteratively informing requirements and specifications, which will include articulating findings and working with researchers to adjust specifications and code as interim results are found 

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