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Non Union Football Data Analytics Jobs (NOW HIRING)

SumerSports LLC

Sr. Football Data Scientist

Build and implement analytical football models, providing valuable insights to enhance our football analytics offerings. Conduct comprehensive data analysis to extract actionable insights. * Model ...

Swish Analytics

Sports Data Analyst

San Francisco, CA · On-site +1

Deep knowledge of a second sport including football, basketball, baseball, hockey or tennis * Exposure to the data science process and tech stack * Anomaly Detection Techniques Swish Analytics is an ...

Bay Football Club

Head of Data & Analytics

San Jose, CA · On-site

$140K/yr

While our community is our foundation, our love of football is our reason for being. We have the ... Ability to work non-traditional hours, including evenings, weekends, holidays, and travel with the ...

UT Federal Credit Union

Data Analyst

Knoxville, TN · On-site

Ability to collaborate effectively with both technical and non-technical stakeholders. Preferred ... Experience within financial services, banking, or credit unions. * Knowledge of advanced analytics ...

UT Federal Credit Union

Data Analyst

Knoxville, TN · On-site

Ability to collaborate effectively with both technical and non-technical stakeholders. Preferred ... Experience within financial services, banking, or credit unions. * Knowledge of advanced analytics ...

UT Federal Credit Union

Data Analyst

Knoxville, TN · On-site

Ability to collaborate effectively with both technical and non-technical stakeholders. Preferred ... Experience within financial services, banking, or credit unions. * Knowledge of advanced analytics ...

SumerSports LLC

Product Manager

... Football Data Analytics and AI experiences for our customers. It's a complex but exciting task to ... non-invasive way and iterate as needed. Job Overview: As we are a small company, this Product ...

SumerSports LLC

Sr. Product Manager

$129K - $170K/yr

... Football Data Analytics and AI experiences for our customers. It's a complex but exciting task to ... non-invasive way and iterate as needed. Job Overview: As we are a small company, this Senior ...

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Non Union Football Data Analytics information

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

To thrive as a Non Union Football Data Analyst, you need strong quantitative analysis skills, a background in statistics or data science, and a deep understanding of football tactics and performance metrics. Proficiency with data analytics tools such as Python, R, SQL, and specialized sports analytics software is typically required. Excellent attention to detail, effective communication, and the ability to translate complex data into actionable insights are critical soft skills for this role. These skills and qualities are essential for identifying trends, informing coaching decisions, and providing a competitive edge through data-driven strategies.

What is non union football data analytics?

Non union football data analytics involves collecting, processing, and interpreting data related to football (soccer or American football) performance, statistics, and strategy, outside of unionized organizations. Professionals in this field use statistical methods, software tools, and data visualization techniques to help teams, coaches, and management make better decisions. This role typically focuses on providing insights about player performance, match outcomes, and tactical trends, often for private companies, media, or non-union clubs. The 'non union' aspect means that the role is not governed by a labor union, which can affect job conditions and benefits.

Do NFL teams hire data analysts?

Yes, NFL teams hire data analysts to evaluate player performance, develop strategies, and improve decision-making using statistical tools and data analysis techniques. These roles often require knowledge of sports analytics software, programming languages like Python or R, and a strong understanding of football metrics.

What are some typical challenges faced by professionals in Non Union Football Data Analytics roles?

Professionals in Non Union Football Data Analytics often encounter challenges such as consolidating and cleaning large, disparate datasets from different sources to ensure data accuracy. They must also communicate complex analytical findings to coaches and management in clear, actionable terms. Collaboration with scouts, coaching staff, and IT teams is frequent, requiring strong interpersonal skills and adaptability. Additionally, staying updated with the latest analytical tools and football trends is essential for delivering valuable insights that can influence team strategies.

How to get into data analysis in football?

To pursue a career in football data analysis, develop skills in statistics, data management, and programming languages like Python or R. Gain experience with sports analytics tools, understand football tactics, and consider obtaining certifications in data analysis or sports management to enhance your qualifications.

Can a data analyst become a sports analyst?

A data analyst can transition into a sports analyst role by gaining knowledge of sports-specific metrics, understanding game strategies, and developing expertise in sports data sources. Skills in data visualization, statistical analysis, and tools like SQL or Python are valuable in both roles. Certifications or experience in sports analytics can also facilitate this career shift.

How much do NFL data analysts make?

NFL data analysts typically earn between $60,000 and $100,000 annually, depending on experience, education, and the level of responsibility. Entry-level analysts may start lower, while experienced professionals with advanced skills in data visualization and statistical software can earn higher salaries in sports organizations or consulting firms.
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Non Union Football Data Analytics jobs near you
Sports Data Analyst

Sports Data Analyst

Swish Analytics

San Francisco, CA

Full-time

Re-posted 16 days ago

Job description

Company Description

Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We're looking for team-oriented individuals with an authentic passion for accurate and 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 systems to support products across a variety of industries and consumer/enterprise clients.

Duties:

  • Work closely with Data Scientists and Engineers to diagnose and treat data pipeline integrity issues

  • Detect data inaccuracies such as missing, out of range or otherwise incorrect on-field data

  • Source origins of data inaccuracies through data pipeline dependencies and python code base

  • Define data validation tests to flag future game errors

  • Research accurate roster active statuses, primary positions and game participation

  • Validate data changes after logic updates

  • Production model feature deep dives to explain project market lines

  • Clearly document findings

  • Develop intimate familiarity with existing databases and construct metadata references

  • With guidance, support lead Data Scientists in feature development and model analysis

Requirements:

  • Bachelor's Degree in Computer Science, Data Science or similar major

  • Minimum of 1 year of experience in football data analysis

  • Deep knowledge of football, basketball or baseball; including roster compositions of professional and college teams, general gameplay strategies, and typical in-game scenarios

  • Data Extraction, Wrangling and Analysis in Python

  • Strong SQL querying skills

  • Attention to detail

Preferred:

  • Strong Python data management programming skills

  • Data Visualization experience with a user application like Streamlit

  • Deep knowledge of a second sport including football, basketball, baseball, hockey or tennis

  • Exposure to the data science process and tech stack

  • Anomaly Detection Techniques

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.