Stats Perform

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Engineering Team Lead (IA/ML)

Chicago or Remote

Machine Learning Operations Engineering Team Lead


Stats Perform brings unmatched depth and breadth of data, sports research, news and video content, and unrivaled AI-powered solutions to sports media and broadcasters, technology companies, global brands, sportsbooks, teams and leagues, and fantasy providers.

Stats Perform’s extensive list of customers includes four of the top-five most popular global sports broadcast companies, seven of the top-10 global tech companies, all of the top-10 sportsbooks and seven of the top-10 football (soccer) franchises. We collect more than 30 million unique data points and distributes them to more than 1,800 customers, reaching over three billion fans a year. Stats Perform employs more than 1,600 full-time employees across 25 countries and is home to the largest sport-focused AI team with more than 40 artificial intelligence scientists collaborating with over 100 engineers creating AI solutions. These innovations will be the foundation of the future strategy of the new entity allowing rights-holders, leagues, media, and gaming partners to derive the most value and develop the richest experiences for over 3 billions sports fans.


We are looking for a Technical and Team Lead for our Machine Learning Engineering team to lead the design, scoping, and building of our world-class machine learning platform solutions. You will be responsible for empowering our AI data scientists by developing a collection of industry-strength platform services, tools, and playbooks to enable them to rapidly productize and deploy our AI/ML models to satisfy real-time, batch, and on-demand use cases. Responsibilities include, but not limited to:

  • Manage the Machine Learning engineering team
  • Lead technical design discussions within the Machine Learning engineering team and with other engineering managers in the organization.
  • Collaborate with the AI team, Product Managers, and IT to enable deployment and productization of AI/ML models
  • Lead the Machine Learning Engineering team to build services, tools, and playbooks.
  • Work with Data Engineering teams, AI teams, and IT teams to manage the ML lifecycle, including data prep, training data generation, feature engineering, optimization, experimentation, reproducibility, deployment and end-to-end workflow management
  • Identify, assess and implement 3rd party technologies that may complement Stats Perform capabilities, and accelerate advancement of critical features; maintain strong collaborative relationships with 3rd party technology providers


We are looking for a person who is comfortable wearing several different hats. This role will routinely require switching between being an AI/ML Data Scientist, a traditional Software Engineer, a DevOps Engineer, and a Tech Lead.

AI/ML Engineering Experience

  • 5+ years of relevant industry experience in Data & analytics platform or machine learning and data science
  • Experience managing engineering teams, especially teams building services and tools for AI/ML ecosystems
  • Technical hands-on expertise with building enterprise grade machine learning and data platforms
  • Track-record of successfully launching ML projects in production
  • Experience with AWS services; Lambda, Kafka, Kinesis, S3, and other tools use in ML ecosystems
  • Experience with Python or other relevant AL/ML software engineering languages
  • Understanding of CI/CD pipelines to in various environments utilizing technologies such as Kubernetes, Docker, and various cloud-based environments


  • Bachelor’s degree in Engineering, Computer Science, Mathematics, Computational Statistics, Machine Learning or related STEM fields
  • Verbal/written communication and presentation skills, including an ability to effectively communicate with both business and technical teams, and both internal and external stakeholders
  • An open minded, structured thinker
  • A team player and good teammate
  • Intellectual curiosity and excellent problem-solving skills, including the ability to structure and prioritize an approach for maximum impact

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