James Nesbit

Senior Applied Scientist
World Wide Return and ReCommerce Planning
Amazon

[email protected]
+1 (646) 823-0740
Curriculum Vitae

I'm James Nesbit, a Senior Applied Scientist at Amazon with a Ph.D. in Economics from NYU. My work focuses on forecasting, supply chain optimization, causal inference, and machine learning. I develop interpretable time series models and optimization algorithms that drive business value through improved planning accuracy and resource allocation.

Professional Experience

Amazon

World Wide Return and ReCommerce Planning (January 2025-Present)

As a Senior Applied Scientist, I architect forecasting and supply chain planning platforms for Amazon's reverse logistics supply chain. I develop interpretable time series models with built-in decomposition capabilities, establish statistical forecast goal planning frameworks, and create causal inference capabilities for data-driven policy adjustments.

F3 Distribution Optimization and Grocery Innovation (March 2021-January 2025)

I designed and implemented optimization algorithms for transportation planning, optimal replenishment, inventory planning, and store layout optimization. I built solutions using Java, Python/SQL, and JavaScript/TypeScript on AWS infrastructure, and collaborated with forecasting science teams to integrate time series models into optimization frameworks.

Technical Skills

Programming Languages: Python, Java, R, SQL, R, React

Cloud & Tools: AWS (CDK and Python SDK)

Areas of Expertise: Time Series Forecasting, Optimization, Causal Inference, MLOps

Publications and Forthcoming

A Robust Machine Learning Algorithm for Text Analysis
(with Shikun Ke and José Luis Montiel Olea)
Quantitative Economics Vol. 15(4), 2024, pp. 939-970
bibtex

Short-Term Fluctuations in Incidental Happiness and Economic Decision-Making: Experimental Evidence from a Sports Bar
(with Judd Kessler, Andrew McClellan, and Andrew Schotter)
Experimental Economics Vol. 25(1), 2024, pp. 141-169
bibtex

(Machine) Learning Parameter Regions
(with José Luis Montiel Olea)
Journal of Econometrics, Vol. 222(1), 2021, pp. 716-744
bibtex, code, online supplement, working paper version

Working Papers

Text as Instruments
March 2021
bibtex

Education

Ph.D. in Economics, New York University (2015–2021)
Advisors: Tim Christensen, José Luis Montiel Olea, Alfred Galichon