Articles about DraftKings

Lead end-to-end personalization ML initiatives: build scalable ML pipelines, design CI/CD for models, monitor production performance, implement retraining and drift detection, partner with cross-functional teams, and mentor engineers.

Design, build, and scale real-time and batch data infrastructure and pipelines (Kafka, Databricks, Snowflake) to power analytics, reporting, and AI-powered data experiences. Drive data modeling, governance, observability, and best practices while partnering with cross-functional teams and mentoring engineers.

Lead end-to-end personalization data science initiatives: design and deploy machine learning and statistical models into production, optimize recommendations and content layouts, partner with engineering/product/analytics, monitor performance, and mentor junior scientists.