Teams at Jackpocket

Lead data science initiatives from conception through deployment, developing statistical models and machine learning algorithms for trading intelligence. Analyze large datasets, integrate solutions into production systems, support automated trading strategies, communicate insights to stakeholders, and mentor junior data scientists. Collaborate cross-functionally to improve trading processes, product offerings, and business operations.

Build, scale, and operate Databricks and AWS infrastructure supporting automated, reproducible machine learning workflows. The role includes infrastructure-as-code with Terraform or Pulumi, containerized platforms using Docker and Kubernetes, system architecture, reliability and cost optimization, and cross-functional collaboration with data and ML teams. The engineer will lead complex projects, improve platform scalability and usability, document systems, and mentor peers while operating production-scale data and ML platforms.

Develop statistical and machine learning models for DraftKings Sportsbook, engineer sports data assets, implement pricing engines in Python, and integrate models into production applications. Build automated tests, validate data flows, support deployment and monitoring across the machine learning lifecycle, and collaborate with Trading, Product, Engineering, and QA. Coach junior data scientists and contribute to sports modeling solutions.