Job description

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 include GPU availability/lifecycle, latency measurements from end users to data centers, game performance across different GPU types, and queuing information. Our active projects include applying optimization techniques to the cloud gaming experience; developing user behavior profiling, user base segmentation, actionable cluster detection, effective personalized recommendations, lifetime value analysis, and critical areas such as 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 (Python, SQL, Delta Lake, Apache Spark, Databricks, MLflow, Grafana, Elasticsearch). What You will be Ding: • 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. What We Need to See: • 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). • 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. Ways to Stand Out from the Crowd • Good interpersonal and presentation skills in working with multiple partners, adept at explaining intricate analytical solutions and their business implications. • Experience in time series analysis and forecasting for demand prediction in optimization contexts is a plus. • Experience in active ML production pipelines (MLflow, Kubeflow) with a focus on deploying and monitoring optimization models is a plus. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 1, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. #deeplearning

Requirements

Must-haves

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.
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.
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.
Ways to Stand Out from the Crowd
Good interpersonal and presentation skills in working with multiple partners, adept at explaining intricate analytical solutions and their business implications.
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until September 1, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
#deeplearning

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

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