Senior Data Scientist, Cloud Gaming - Prescriptive Analytics and Optimization

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +1 · Remote

This role focuses on building and deploying ML/AI and optimization models for a cloud gaming platform, specifically for real-time routing, scheduling, capacity management, and user experience enhancement. It involves data ingestion, processing, analysis, anomaly detection, and predictive modeling, with a strong emphasis on prescriptive analytics and optimization techniques. The role also leverages agentic AI for automation.

What you'd actually do

  1. 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.
  2. Develop reusable framework deployments for data ingestion, processing, and analysis to support dynamic user interventions for targeted business outcomes.
  3. 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.
  4. Identify, analyze, and interpret trends or patterns in complex data sets using supervised and unsupervised learning techniques, informing prescriptive solutions.
  5. 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.

Skills

Required

  • 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
  • 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)
  • Experience with libraries or tools relevant to optimization (e.g., Google OR-Tools)
  • Experience with common tools for data storage and processing, including drilling into problems of running large-scale software across large clusters
  • Strong experience in data cleaning, aggregation, transformation, and extraction

Nice to have

  • Experience in time series analysis and forecasting for demand prediction in optimization contexts
  • Experience in active ML production pipelines (MLflow, Kubeflow) with a focus on deploying and monitoring optimization models

What the JD emphasized

  • prescriptive analytics and optimization
  • AI/ML, prescriptive modeling, and optimization methodologies
  • optimization techniques
  • agentic AI

Other signals

  • optimization models
  • prescriptive analytics
  • real-time routing and scheduling
  • user behavior profiling
  • capacity management
  • latency minimization
  • agentic AI