Swish Analytics16.02.26
AI SCORE 8.5

Remote NFL Data Scientist - Predictive Sports Analytics

$100K–$130K/year

About the Role

Swish Analytics is seeking a talented Remote NFL Data Scientist to join our innovative team. As a Remote NFL Data Scientist, you will play a pivotal role in developing predictive sports analytics data products that redefine the sports betting landscape. This position allows you to work remotely from anywhere in the USA while contributing to a fast-paced, creative environment focused on technical excellence.

What You'll Do

  • Ideate, develop, and enhance machine learning and statistical models that drive Swish’s core algorithms for state-of-the-art sports betting products.
  • Utilize sports-specific domain knowledge to create contextualized feature sets that improve model accuracy.
  • Contribute to all stages of model development, from proof-of-concept creation to beta testing and deployment in collaboration with data engineering and product teams.
  • Continuously strive to enhance model performance through rigorous offline and online experimentation.
  • Analyze results and outputs to assess model performance, identifying weaknesses to direct future development efforts.
  • Adhere to software engineering best practices and contribute to shared code repositories.
  • Document modeling work and present findings to stakeholders and technical teams.

Requirements

  • 3+ years of experience as a Data Scientist, with a focus on machine learning and statistical modeling.
  • Strong programming skills in Python or R, with experience in libraries such as TensorFlow, scikit-learn, or similar.
  • Experience in sports analytics or a strong passion for sports, particularly NFL.
  • Proficient in data manipulation and analysis using SQL and data visualization tools.
  • Ability to work independently and collaboratively in a remote team environment.
  • Excellent communication skills to present complex ideas clearly to non-technical stakeholders.

Nice to Have

  • Experience with cloud platforms such as AWS or Google Cloud.
  • Familiarity with agile development methodologies.
  • Knowledge of sports betting markets and strategies.

What We Offer

  • Competitive salary ranging from $100,000 to $130,000 annually.
  • Flexible remote work environment with a focus on work-life balance.
  • Opportunities for professional growth and development in a dynamic industry.
  • Collaborative team culture that values innovation and creativity.
  • Health, dental, and vision insurance plans.
  • Generous paid time off and holiday schedule.
Why This Job8.5 of 10

This Remote NFL Data Scientist role at Swish Analytics offers a unique opportunity to work in the exciting field of sports analytics with a competitive salary and flexible work environment.

Salary Range
Required
0/1
Optional
0/1
Bonus
0/1

Who Will Succeed Here

Proficiency in Python and R for advanced statistical modeling and machine learning, with experience using libraries like Pandas, NumPy, and Scikit-learn to analyze sports data.

Strong self-discipline and time management skills to excel in a fully remote work environment, ensuring productivity and collaboration with a distributed team.

A robust analytical mindset with a proven track record of developing predictive models for sports analytics, particularly in the context of the NFL, showcasing a deep understanding of statistical techniques and data visualization tools like Tableau or Matplotlib.

Learning Resources

Python for Data Science Handbookguide

Career Path

Remote NFL Data Scientist - Predictive Sports Analytics(Now)Senior Data Scientist - Sports Analytics(2-4 years)Lead Data Scientist / Director of Predictive Analytics(4-6 years)

Market Overview

Market Size 2024
$5.2B
Annual Growth
14.3%
AI Adoption in Sports Analytics
65%
Investment in Sports Tech
+25%
Labour Demand for Data Scientists
+22%
Avg Salary for Data Scientists
$120K

Skills & Requirements

Required
PythonRMachine Learning
Growing in Demand
Deep LearningBig Data TechnologiesCloud Computing (AWS, Azure)
Declining
Excel for Data AnalysisTraditional Statistical Packages (e.g., SPSS)

Domain Trends

Increased Use of Machine Learning
Over 70% of NFL teams are incorporating machine learning models to predict player performance and game outcomes, leading to more data-driven decision making.
Rise of Real-Time Analytics
Real-time data analytics is becoming crucial, with 60% of teams using live data feeds during games to adjust strategies dynamically.
Growing Focus on Player Health Analytics
With injuries costing teams millions, 55% of teams are investing in predictive analytics for player health, utilizing data to forecast injury risks.

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