Senior Data Scientist, Cloud Gaming - Prescriptive Analytics and Optimization

Build and deploy scalable ML/AI and optimization models to enhance demand forecasting, optimize capacity allocation, and develop user-specific feature engineering for real-time cloud gaming services.

Develop reusable framework deployments for data ingestion, processing, and analysis to support dynamic user interventions for targeted business outcomes.

Acquire and apply domain knowledge of the product and software stack to identify and drive the resolution of data inconsistencies and improve model performance, especially in the context of optimization outcomes.

Identify, analyze, and interpret trends or patterns in complex data sets using supervised and unsupervised learning techniques, informing prescriptive solutions.

Design and implement improvements to real-time prescriptive scheduling pipelines, using techniques like linear programming and constraint optimization, to enhance capacity utilization and user retention.

Improve productivity of the organization by mining petabytes of data for actionable insights for business and engineering, often through prescriptive recommendations.

Collaborate with a variety of partners to understand requirements, design robust solutions, and guide the team to deliver impactful results.

Leverage agentic AI to deliver best-in-class automation and programming solutions for complex analytical problems.

BS/MS (or equivalent experience) with 6+ years of experience or PhD in Data Science, Computer Science, Operations Research, Statistics, Applied Mathematics, or related quantitative fields, with a strong emphasis on prescriptive analytics and optimization.

Strong background knowledge and practical experience in probability, statistics, AI/ML, prescriptive modeling, and optimization methodologies (e.g., linear programming, network flow, decision theory, and multi-armed bandit).

Strong coding skills, including the ability to write readable, testable, maintainable, and extensible code (primarily Python), with experience in libraries or tools relevant to optimization (e.g., Google OR-Tools).