Nathaniel Price, Ph.D.

Washington, D.C. +1 904 315 2486 natbprice@gmail.com natbprice.github.io

Experience

ICF, Reston, Virginia, US (Remote)
Lead Data Scientist | March 2022 - Present
Senior Data Scientist | May 2020 - March 2022
Data Scientist | October 2019 - May 2020

Built Azure cloud computing/machine learning infrastructure from the ground up for low-cost, scalable analysis of billions of records of utility smart meter data
Researched, prototyped, and deployed machine learning models related to utility analytics (e.g., energy disaggregation, energy savings, EV detection)
Established cross-team Data Science Knowledge Share meetings to promote collaboration and information sharing
Assisted other teams in scaling data science processes by advising on best practices and providing technical assistance

University of Nebraska-Lincoln, Lincoln, Nebraska, US
Data Scientist | September 2016 - October 2019

Designed, developed, and deployed open-source, web-based, data analysis application (SQL, R, Shiny) for analyzing repeat-purchase behavior (recruitment, retention, churn, reactivation) of Nebraska sportspersons
Mentored graduate students and facilitated data science research resulting in multiple journal publications, international conference presentations, and a book chapter

University of Florida, Gainesville, Florida, US
Graduate Research Assistant | August 2012 - July 2016

Integrated machine learning (e.g., Gaussian process) and optimization to design engineering systems considering uncertainty in future decision making process
Collaboratively developed optimization-based solution to The NASA Langley Multidisciplinary Uncertainty Quantification Challenge (2014)

ONERA - The French Aerospace Lab, Palaiseau, Île-de-France, France
Ph.D. Student Researcher | October 2014 - March 2016

As part of international joint-PhD collaboration between 2 universities (UF, EMSE) and ONERA aerospace lab, developed a novel method for optimal design under uncertainty that incorporated risk of future redesign into design optimization
Co-authored book chapter on advanced space vehicle design under uncertainty

University of Florida, Gainesville, Florida, US
Undergraduate Research Assistant | September 2011 - August 2012

Created parameterized biomechanical model in Python to understand interactions of patient variability and design changes on safety of Biomet rigid sternal fixation device
Awarded Biomedical Engineering Society (BMES) Design and Research Award and Knox T. Millsaps Outstanding Undergraduate Paper Award

SpaceX, Cape Canaveral, Florida, US
Launch Engineer Intern | August 2010 - January 2011

Performed maintenance of launch vehicle ground systems
Ground crew team member during launch of SpaceX COTS Demo Flight 1

Education

Doctorate | Mechanical Engineering
University of Florida, Gainesville, Florida, US | 2016
École des Mines de Saint-Étienne, Saint-Étienne, Rhône-Alps, France | 2016

Graduate Certificate | Scientific Computing
University of Florida, Gainesville, Florida, US | 2014

Master of Science | Mechanical Engineering
University of Florida, Gainesville, Florida, US | 2014

Bachelor of Science | Mechanical Engineering
University of Florida, Gainesville, Florida, US | 2012

Professional Development

DeepLearning.AI TensorFlow Developer Professional Certificate (80 hours), Coursera, Credential ID VDW7KD9SA2TM (2023)

Publications

2 book chapters 5 peer-reviewed journal publications 5 conference papers

Full List Available On Google Scholar: https://scholar.google.com/citations?user=rXaKU0EAAAAJ&hl=en

Select Publications:

Balesdent, Mathieu, Loïc Brevault, Nathaniel B. Price, Sébastien Defoort, Rodolphe Le Riche, Nam-Ho Kim, Raphael T. Haftka, and Nicolas Bérend. 2016. “Advanced Space Vehicle Design Taking into Account Multidisciplinary Couplings and Mixed Epistemic/Aleatory Uncertainties.” In Space Engineering: Modeling and Optimization with Case Studies, edited by Giorgio Fasano and János D. Pintér, 1–48. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-41508-6_1.

Chaudhuri, Anirban, Garrett Waycaster, Nathaniel Price, Taiki Matsumura, and Raphael T. Haftka. 2015. “NASA Uncertainty Quantification Challenge: An Optimization-Based Methodology and Validation.” Journal of Aerospace Information Systems 12 (1): 10–34. https://doi.org/10.2514/1.I010269.

Price, Nathaniel B., Christopher J. Chizinski, Joseph J. Fontaine, Kevin L. Pope, Micaela Rahe, and Jeff Rawlinson. 2020. “An Open-Sourced, Web-Based Application to Improve Our Ability to Understand Hunter and Angler Purchasing Behavior from License Data.” PLOS ONE 15 (10): e0226397. https://doi.org/10.1371/journal.pone.0226397.

Data Science Skills

Cloud Computing: Azure • AWS • high-performance computing (Azure Batch) • NoSQL (Azure Table/Blob, AWS S3) • containers (Docker, Azure ACI, Azure ACR)
Communication: presentations • dashboard design (Shiny) • data analysis reports (Rmarkdown, Jupyter) • data visualization (plotly, ggplot2, leaflet) • peer-reviewed publications (journal, book chapter, conference)
Numerical Methods: optimization (stochastic, genetic, multi-start) • differential equations
Programming Languages: R • Python • SQL • Matlab • C++
Software Development: source control (Git, SVN) • agile development (Jira) • CI/CD (Azure DevOps)
Statistics & Machine Learning: deep learning (Tensorflow) • NLP • surrogate models • uncertainty quantification • Monte Carlo simulation • experimental design • survey methodology

natbprice / resume