Teams at Scientific Games

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.

Design and build the organization's first ML platform: define architecture and roadmap, create self-service deployment frameworks, enable model registry, feature retrieval, inference routing, observability, and safe rollout workflows; mentor engineers and partner with Data Science to drive platform adoption and developer productivity.