Senior Data Scientist, Prescriptive Analytics and Optimization - Cloud Gaming

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI—the next era of computing—with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as “the AI computing company.” We're looking to grow our company and build our teams with the smartest people in the world. Would you like to join us at the forefront of technological advancement?

Our team is building an innovative Data Platform that employs advanced analytics, including prescriptive modeling and constrained optimization, for real-time routing and scheduling at scale. This platform encompasses data collection, processing, visualization, analysis, anomaly detection, root cause identification, and predictive modeling. Our data includes GPU availability/lifecycle, latency measurements from end users to data centers, performance of games on different GPU types, and queuing information. Our active projects include applying optimization techniques for cloud gaming experience, developing user behavior profiling, user base segmentation, actionable cluster detection, effective personalized recommendations, lifetime value analysis, and critical areas like capacity management, prescriptive scheduling, and subscription churn analysis. We also focus on time-series forecasting, decision-making models, resource allocation, and latency minimization. You will wield the power of Data, AI, and Operations Research to help deliver a best-in-class cloud streaming performance and experience to our users across the world. Our technology stack relies on industry-standard components (Google OR-Tools, Kubeflow, Apache Spark, Databricks, MLflow, Delta Lake, Grafana, Elastic Search, Python, SQL).

What you'll be doing:

• 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.

• 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.

• 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.

• Develop reusable framework deployments for data ingestion, processing, and analysis to support advanced analytical models.

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

• 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 programming solutions for complex analytical problems.

What we need to see:

• BS/MS with 5+ 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, and optimization methodologies (e.g., linear programming, network flow, and decision theory).

• An outstanding track record of past projects related to the research and application of data science, particularly involving optimization and prescriptive modeling.

• 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).

• 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, with an understanding of how data quality impacts performance

• Good interpersonal and presentation skills in working with multiple partners, adept at explaining intricate analytical solutions and their business implication.

• Experience in time series analysis and forecasting for demand prediction in optimization contexts is a plus.

• Experience in active ML production pipelines (MLflow, Kubeflow)