Nathan Geldner

I'm an applied scientist with 10+ years of experience applying stochastic optimization, probabilistic simulation, and AI/ML to problems spanning public health, economic policy, and climate adaptation. I specialize in scientific decision-making under uncertainty: formalizing ambiguous real-world problems into tractable mathematical frameworks, designing and deploying rigorous system models (statistical, probabilistic, simulation, ML) on cloud-native infrastructure (AWS, Databricks, Spark), and partnering cross-functionally with engineering and stakeholder teams to deliver actionable guidance. I go beyond modeling potential outcomes—I map the full space of decisions and uncertain futures to isolate the thresholds between success and failure, and build the automated, scalable tooling to make that tractable in production.

How I Work

  • Translating messy real-world problems into concrete mathematical terms
  • Using insights from subject-matter experts with hands-on experience to build mathematical, statistical, and simulation models of the problem
  • Evaluating the outcomes of strategic decisions quantitatively with selective attention to the details that matter to your decision-making.
  • Quality-checks and validation to verify whether a model you have on hand is appropriate and reliable, and to help you understand its limitations.
  • Distilling big datasets of complex model results into actionable insights and coherent narratives using AI/ML data-mining techniques.
  • Managing uncertainty—when is there a surefire win, when is there a smart gamble, and when is it time to hedge your bets because the odds can't be predicted?

Selected Project Documentation

Documentation for some of the projects I've been involved in:

Contact

Get in touch to discuss how I can help you design, improve, or quality-check your decision process or model.

nathangeldnerconsulting@gmail.com