Teams at Scientific Games

Owns product definition quality for complex data product opportunities, from problem framing through Ready-to-Build handoff. Authors product specifications, metric tables, and agent context documents; makes deployment channel decisions; maintains business context layers; and represents product definition in reviews with Platform Engineering and Data Science/ML. Establishes standards, coaches Solutions Analysts without formal authority, and ensures clean technical handoffs and well-defined model requirements.

Build self-service ML platform tooling and golden paths enabling Data Scientists to deploy and monitor models across batch and real-time. Design CI/CD, model registry, deployment workflows, observability standards, SDKs/CLIs, and partner with staff MLEs to shape platform architecture and developer experience.

Build and deploy end-to-end decision science systems (forecasting, experimentation, optimization, pricing, recommendations). Lead production-grade batch and real-time pipelines, partner with MLEs for deployment, drive domain impact, establish modeling standards, and mentor junior data scientists.