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Remote Football Data Science Jobs in Illinois (NOW HIRING)

Benefits Fully remote: work from anywhere in the US, Canada, UK, Ireland, Australia, and New ... scientific reasoning, and data-driven insights, for technical accuracy and real-world validity.

Data Science Intern

Chicago, IL · On-site +1

$1.20K/wk

Data Science Intern at Rundoo Our mission is to empower independent supply stores with best-in ... final call (remote) - A closing conversation with our CEO. About Our Founders * Andrew (CTO): ...

Director of Data Science

Chicago, IL · On-site +1

$153.20K - $229.80K/yr

Dir Data Science - GD06AE We're determined to make a difference and are proud to be an insurance ... Candidates who do not live near an office may be considered for a remote work arrangement with ...

This is a fully remote, flexible contract role. No prior AI industry experience required - just deep, hands-on command of data science and the ability to think critically about how models reason.

... data science solutions are scalable, reliable, and aligned to strategic priorities. WORK AUTHORIZATION & LOCATION REQUIREMENT This position is primarily remote but requires candidates to reside ...

... data science solutions are scalable, reliable, and aligned to strategic priorities. WORK AUTHORIZATION & LOCATION REQUIREMENT This position is primarily remote but requires candidates to reside ...

... data science solutions are scalable, reliable, and aligned to strategic priorities. WORK AUTHORIZATION & LOCATION REQUIREMENT This position is primarily remote but requires candidates to reside ...

Data Scientist

Chicago, IL · On-site +1

$90.16K - $135.24K/yr

... science team responsible for designing and delivering powerful analytical insights utilizing ... This role can have a Hybrid or Remote work arrangement depending on experience and skillset.

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Remote Football Data Science information

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

To thrive as a Remote Football Data Scientist, you need a solid background in statistics, data analysis, and programming (often in Python or R), typically supported by a relevant degree in data science, mathematics, or a related field. Familiarity with sports analytics platforms, machine learning frameworks, and database systems like SQL is commonly required, as are certifications in data analysis or analytics. Strong problem-solving abilities, effective communication, and the capacity to collaborate virtually are vital soft skills for success in this remote, data-driven environment. These skills and qualities enable accurate insights, informed decision-making, and seamless teamwork, ultimately driving competitive advantage for football organizations.

What are the typical challenges faced by remote football data scientists, and how can they effectively collaborate with coaching and analytics teams?

Remote football data scientists often face challenges in accessing real-time data, maintaining clear communication across different time zones, and ensuring their analyses align with the needs of coaches and analysts. To overcome these hurdles, it's important to establish regular virtual meetings, use collaborative tools like shared dashboards, and maintain clear documentation of methodologies and findings. Building strong relationships with on-site team members and staying proactive in communication can significantly enhance collaboration and ensure that data-driven insights effectively support team strategies.

What is a Remote Football Data Scientist?

A Remote Football Data Scientist is a professional who analyzes football (soccer or American football) data to provide insights that help teams, organizations, or media make informed decisions. They use statistical analysis, machine learning, and data visualization techniques to evaluate player performance, predict outcomes, and optimize strategies—all while working remotely. These professionals often collaborate with coaches, scouts, analysts, and other stakeholders by sharing their findings through reports and digital platforms. The role requires strong skills in programming, mathematics, and domain knowledge of football. Working remotely allows them to contribute from anywhere, often using cloud-based tools and virtual collaboration platforms.

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

AspectRemote Football Data ScienceRemote Sports Data Analyst
Required CredentialsDegree in Data Science, Statistics, or related field; knowledge of football analyticsDegree in Sports Management, Data Analysis, or related; familiarity with sports data
Work EnvironmentRemote, often collaborative with football teams or analytics firmsRemote or on-site, working with sports organizations or media outlets
Industry UsagePrimarily in football clubs, analytics companies, or sports tech startupsMedia, broadcasting, or sports organizations analyzing various sports

Remote Football Data Science focuses specifically on football analytics, requiring specialized knowledge of football metrics and data science skills. In contrast, Remote Sports Data Analysts work across multiple sports, analyzing diverse datasets. Both roles often work remotely and require similar analytical credentials, but their industry focus and specific expertise differ.

What are the most commonly searched types of Football Data Science jobs in Illinois? The most popular types of Football Data Science jobs in Illinois are:
What cities in Illinois are hiring for Remote Football Data Science jobs? Cities in Illinois with the most Remote Football Data Science job openings:
Data Science Consultant

Data Science Consultant

DataAnnotation

Springfield, IL • On-site, Remote

$40/hr

Full-time

This job post has expired today. Applications are no longer accepted.


Job description

Join the DataAnnotation team and contribute to developing cutting-edge AI systems, while enjoying the flexibility of remote work and setting your own schedule. We are looking for experienced quantitative professionals to help advance AI development. AI models are increasingly capable of performing complex analytical and scientific reasoning — but these systems still need practitioners with real-world quantitative experience to validate whether the outputs actually hold up in practice.

That's where you come in. As a member of DataAnnotation's team, you'll work closely with state-of-the-art AI models on tasks like evaluating AI-generated quantitative analysis, solving technical problems, and providing feedback that directly shapes how these systems reason about data, models, and scientific problems. Whether your background is in data science, astrophysics, economics, biostatistics, operations research, or any other quantitative field, if you think rigorously about data and models, your skills are directly applicable here.

Some team members fit this work alongside a full-time role, while others treat it as their primary focus. To get started, once you sign up for an account, you'll take a short assessment (this serves as our version of an interview). If you pass, you'll receive an email confirmation, and paid work will become available on our platform.

Benefits Fully remote: work from anywhere in the US, Canada, UK, Ireland, Australia, and New Zealand. Flexible schedule: choose which projects you take on and when you work. Competitive pay: projects are paid hourly, starting at $40+ USD per hour.

Impact: help shape the future of AI systems built to reason about data and analytics. Responsibilities Evaluate AI-generated quantitative work, including statistical analysis, predictive modeling, scientific reasoning, and data-driven insights, for technical accuracy and real-world validity. Design and solve quantitative problems used to train and benchmark AI systems, spanning areas like forecasting, experimental analysis, optimization, and statistical inference.

Write clear technical explanations and well-documented analytical code. Provide feedback that directly shapes the next generation of AI models built for quantitative reasoning. Qualifications 2+ years of hands‐on experience in a quantitative role or research environment — such as data science, statistics, economics, finance, physics, biology, epidemiology, operations research, or any adjacent field.

Some coding experience required, with comfort writing and reviewing analytical code end-to-end. Practical experience with statistical methods, predictive modeling, and experiment design (e.g., A/B testing, hypothesis testing, regression, classification, time‐series forecasting). Fluency in English (native or bilingual level) with strong writing skills.

A bachelor's degree in a quantitative field is preferred (Statistics, Computer Science, Mathematics, Engineering, or similar); a master's or PhD is a plus. Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or equivalent demonstrated expertise). Note: Payment is made via PayPal.

We will never ask for any money from you. This job is only available to those in the US, Canada, UK, Ireland, Australia, and New Zealand. #datascience #J-18808-Ljbffr