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Supports the development, testing, deployment, and operation of machine learning solutions for DraftKings’ Sportsbook platform. Works with experienced data science and machine learning engineers on production systems, scalable model deployment, internal R&D, and technical projects. Collaborates with a mentor while learning end-to-end engineering and data science workflows.

Contribute to production software supporting DraftKings’ sports technology platform. Collaborate with backend and web development teams, build scalable features, write testable and efficient code, apply agile methodologies, and work with mentors and cross-functional technology teams. Interns will gain practical experience solving customer and business problems while developing software engineering skills.

Manage real-time sports betting markets for golf, including data feeds, bet settlement, compilation, price management, and in-play trading. Meet performance KPIs, maintain compliant operations, manage content across channels, collaborate with trading and product teams, and identify efficiency improvements. The role requires strong sports betting knowledge, analytical judgment, Excel proficiency, communication skills, and flexibility for shifts, weekends, and peak sports seasons.

Develop statistical and machine learning sports models, engineer data assets, implement pricing engines in Python, and integrate solutions into Sportsbook applications. Build automated tests, validate data flows, support model deployment and lifecycle management, collaborate with Trading, Product, Engineering, and QA, and mentor junior data scientists.

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.