Washington, D.C. 
+1 904 315 2486 
natbprice@gmail.com 
natbprice.github.io
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
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
- DeepLearning.AI TensorFlow Developer Professional Certificate (80 hours), Coursera, Credential ID VDW7KD9SA2TM (2023)
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.
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