Applied Scientist, AI Economics (TokenOps, FinOps)
Core
Design and implement probabilistic forecasting and stochastic optimization models for TokenOps and FinOps to guide cost, latency, and delivery decisions under uncertainty.
Role type
Senior Applied Scientist (AI Economics/Decision Science)
Builds
Production decision support systems for model routing, capacity planning, and cost optimization in enterprise AI environments.
Domain
Enterprise AI infrastructure, FinOps, TokenOps, and stochastic optimization.
Deliverable
production ML models
Required skills
Probabilistic forecasting, stochastic optimization, causal inference, conformal prediction, Monte Carlo simulations, MLOps, Python, CVaR, chance constraints
Preferred skills
Experience in AI economics, FinOps, or TokenOps; published work in decision science; expertise in telemetry and attribution data analysis
Technologies
Python, MLOps frameworks, probabilistic modeling libraries
Responsibilities
Develop probabilistic models forecasting cost, token demand, and latency as distributions; design stochastic optimization methods for routing and capacity; apply risk measures like CVaR to ensure recommendations meet customer tolerances; implement production monitoring and validation for predictive models; translate quantitative outputs into clear customer recommendations and decision thresholds.
Seniority
Senior, hands-on IC
