Machine Learning Engineer (Zelus Analytics) - Global Remote

Teamworks, the Operating System for Sports™, powers more than 6,500 sports organizations worldwide, including collegiate athletic departments and teams across all major professional leagues. With almost 500 exceptional employees located across over a dozen countries, Teamworks’ software solutions drive the operations of the most recognizable sports properties in the world.

Zelus Analytics, now part of Teamworks, is a premier sports intelligence platform that partners with teams across the NBA, MLB, NFL, IPL, NHL, and European football leagues. As a key component of Teamworks, Zelus continues to deliver cutting-edge analytics, empowering these organizations with advanced data insights to enhance performance, strategy, and decision-making. The platform integrates comprehensive sports data, enabling teams to gain a competitive edge through rich analysis and innovative solutions tailored to their specific needs.

We are seeking a passionate and highly skilled Machine Learning Engineer to join our Analytics team. This role is crucial in advancing our market-leading sports intelligence platform, helping professional teams across various sports leverage cutting-edge machine learning models to compete at the highest levels. Your work will directly impact our exclusive network of partners as they strive to win championships.

This is a fantastic opportunity to be at the forefront of sports analytics technology and to contribute to the ongoing success and rapid expansion of one of the fastest-growing companies in sports tech.

Responsibilities:

• Optimize, automate, and validate quantitative models using machine learning, statistics, optimization, and simulation techniques.
• Develop, schedule, monitor, and maintain robust model training and prediction workflows to ensure optimal performance.
• Collaborate with the broader engineering team to implement infrastructure changes that support one or more sports within our platform.
• Deploy REST APIs on top of fitted models, utilizing distributed computation to enable real-time integration with client-facing applications.
• Work closely with data scientists to define and manage the productionalization of models and the release of platform updates.
• Develop and maintain abstractions for model deployment, ensuring workflows run efficiently and are easily adaptable to future use cases.
• Assess, provision, monitor, and maintain the appropriate infrastructure and tooling to execute model training and prediction workflows.
• Create visualizations using dashboards or application development frameworks to deliver data insights to our clients.
• Collaborate and communicate effectively in a distributed work environment, contributing to a dynamic and innovative team.
• Engage in rotating platform support duties, ensuring the reliability and performance of our machine learning systems.

Requirements:

• Demonstrated experience in software design and development, with a strong foundation in machine learning, statistics, optimization, and simulation.
• Hands-on experience developing and deploying machine learning models in cloud-based environments, with distributed computing expertise.
• Fluency in Python (preferred), with experience in R or other statistical programming languages. 
• Familiarity with front-end development languages such as Javascript or Typescript, along with UI frameworks like React or Vue, is a plus.
• Proficiency in working with relational databases and SQL, optimizing data management in large-scale systems.
• Experience working with Linux servers in virtualized or distributed environments, ensuring stable and secure operations.
• Strong problem-solving skills, with the ability to adapt to evolving technical landscapes and workflows.

Desired Traits:

• Takes initiative, proactively manages multiple priorities, and works well under pressure.
• Analytical, creative thinker, and strategic problem-solver.
• Delivers fast but carefully thought-out, high-quality results.
• Structured, organized, and efficient work style.
• Factual, direct communication style.
• Experience working asynchronously with coworkers across different time zones.
• Interested in managing engineers and