Research Scientist, Infrastructure Modeling and Reliability

What you'll do

• Set modeling strategy for power consumption, peak risk, and reliability tradeoffs across infrastructure.
• Develop, evaluate, and monitor forecasting, statistical, machine-learning, and optimization models for operational decisions.
• Lead cross-functional initiatives that translate model results into infrastructure planning, utilization, and investment decisions; mentor technical contributors.

What they ask for

• 10+ years developing quantitative modeling systems.
• Experience leading cross-functional technical programs from problem definition through deployment and business impact.
• Experience coding in Python, R, C++, Java, or similar languages, and communicating technical concepts and tradeoffs to diverse audiences.
• PhD in a related quantitative field, or equivalent practical experience; the posting also lists a bachelor's degree or equivalent practical experience.

Nice to have

• Experience with forecasting, uncertainty quantification, reliability, infrastructure or energy systems, or large-scale resource planning.
• Record of technical leadership, external publications or engagement, and mentoring senior technical contributors.

Pay

$271,000/year to $347,000/year USD base salary + bonus + equity

Tools

Python, R, C++, Java

AI focus

Develops statistical and machine-learning models to forecast power demand, estimate peak distributions, quantify uncertainty, and support reliable infrastructure decisions.