Our story
Cinderbay Decision Institute was founded by operators and researchers who saw a gap: leaders face probabilistic reality daily, yet most training stops at dashboards. We built a curriculum that turns uncertainty into a managed asset, blending Bayesian inference, causal design, and robust optimization into executive practice.
Mission
Elevate the world’s decision quality by teaching leaders to frame, quantify, and communicate uncertainty responsibly.
Approach
Rigor with translation—mathematics that maps to governance, incentives, and the real costs of error.
Impact
From pricing to product bets and portfolio allocation, our alumni drive measurable ROI and fewer unforced errors.
Principles we teach
- Start with a decision, not a dataset.
- Make assumptions explicit; audit them continuously.
- Prefer causal explanations over descriptive comfort.
- Quantify uncertainty and propagate it through outcomes.
- Optimize for value under constraints, not vanity metrics.
- Build explainability and fairness into every intervention.
Leadership team
Elena Mora, PhD
Founder & Dean — Bayesian decision analysis, previously led pricing science at a global marketplace. Author of “Posteriors in the Boardroom.”
Raj Patel
Director of Causality — Built experimentation platforms at scale, specializes in uplift modeling and policy evaluation.
Maya Greene
Head of Optimization — Operations research and robust planning for supply chains and workforce allocation.
Lars Nyström
Faculty, Forecasting — Probabilistic forecasting, calibration, and policy decision support.
Ethics pledge
We commit to responsible modeling, transparent reporting, and continual harm assessment. We never promise certain outcomes; we teach you to quantify uncertainty and disclose limitations.