Applied Scientist, AI Economics (TokenOps, FinOps)
Tasks
- Apply CVaR and chance constraints for risk aware recommendations
- Build conformal and empirical calibration methods
- Create reusable forecast and calibration assessments
- Design stochastic optimization methods under uncertainty
- Develop probabilistic forecasting models for cost and delivery outcomes
- Establish monitoring validation and versioning for deployed models
- Implement production quality scientific software in Python
- Own proactive planning algorithms and predictive model calibration for TokenOps and FinOps decisions
- Partner on fact store data telemetry and lineage
- Run Monte Carlo simulations and scenario analysis
- Translate model outputs into customer decision recommendations
- Use causal inference to assess TokenOps intervention impact
Perks/Benefits
- N/A
Skills/Tech-stack
CVaR | Causal Inference | Chance constraints | Conformal Prediction | Data Lineage | Data Quality | Drift Detection | Empirical Calibration | Experimental Design | MLOps | Machine Learning | Model Monitoring | Model Validation | Monte Carlo | Monte Carlo Simulation | Probabilistic Forecasting | Probabilistic Models | Python | Scenario Analysis | Stochastic Optimization | Uncertainty Quantification
Roles
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