SumerSports

SumerSports

Sr. Football Data Scientist

Remote (United States) · Senior

Sponsorship not specifiedDetected 448 days ago
PythonGitDatabricksMachine LearningSparkData AnalysisData ScienceStatisticsCollaboration

About the role

  • SumerSports is a leading football intelligence technology company that specializes in providing an innovative suite of products for football fans and NFL clubs.
  • What sets us apart is our unique blend of big tech talent, data scientists, and former NFL personnel, who have a combined 600+ years of NFL experience.
  • The ideal candidate will demonstrate both strong technical skills and a proactive mindset, capable of independently taking projects from concept to deployment.

Responsibilities

  • Build and implement analytical football models, providing valuable insights to enhance our football analytics offerings.
  • Work closely with other data scientists, analysts, and stakeholders to understand their needs and deliver high-quality analytical solutions.

Requirements

  • Advanced degree (Masters or PhD) in a quantitative discipline, or equivalent professional experience.
  • 4+ years of experience in data science, preferably in a sports analytics environment. (level commensurate with experience)

Skills

  • Proficiency in Python and experience with Databricks.
  • Strong programming skills in Python, including experience deploying models beyond exploratory notebooks.
  • Documented experience in football analytics, demonstrated through industry experience, public content, or significant independent projects.
  • Solid foundation in statistical modeling and machine learning methodologies.
  • Proven ability to independently identify and resolve data-related challenges in real-world datasets.
  • Our data-driven platform empowers teams with insights and tools to make informed decisions within salary cap constraints.
  • The platform also serves the NCAA, offering insights around the transfer portal and more.

This listing is sourced directly from SumerSports's careers page and normalized into a canonical job model.