Feb 2018 – Current

Senior Data Scientist

Voith Digital Ventures

I explore and analyze various types of real-time IoT data derived from industrial manufacturing and energy production.

  • Created our internal analytics engine from scratch used to produce malfunction reports for hydroelectric power plants.
  • Developed several new algorithms taking cutting-edge peer-reviewed papers and turning them into useable code, specifically the thousand brains theory, a Bayesian isolation forests, and an efficient binned Gaussian mixture model
  • Served as advanced analytics expert to the North American Sales team separating promising tech from hype.
  • Performed anomaly detection and prediction on high-temporal frequency IIoT acoustic and operational data to help inform industrial machine operators when failures may occur and why (root-cause analysis)
  • Rapidly prototyped advanced analytics solutions for hydroelectric, locomotive systems, and industrial engines oftentimes creating simple dashboards and tools for non-technical domain experts

Jan 2015 – Feb 2018

Project Scientist

National Center for Atmospheric Research (NCAR)
  • My work largely focuses on land-atmosphere interactions specifically trying to understand when, where, and how the surface influences moist convection. The goal is then to apply that knowledge to improve the character of the afternoon convection in models and ultimately improve seasonal forecasts and beyond.
  • Executed long-term global climate simulations on multi-processor super computing systems, routinely managing and
    analyzing 10-50 TB of data.
  • Designed and programmed several open-source software packages designed for expediting the use of the most state-of-the-art climate and weather research practices (;; the Local Land-Atmosphere Coupling Data Dashboard, the Single Column Boundary Layer Model).
  • Created and implemented a new parameterization in a global climate model to realistically represent unresolved convective physical processes (
  • Severed on the United Nations World Meteorological Organization Global Energy and Water Exchange working group for local land-atmosphere coupling to outline strategies for better predicting water resources and the water cycle

Aug 2012 – Jan 2015

Postdoctoral Fellowship

George Mason University
  • Developed a new framework for exploring and describing cloud initiation that uses existing standard observed data.
  • Explored the connection between surface moisture and where and when cumulus clouds form



Atmospheric Science, PhD

University of Michigan

I studied atmospheric science and completed a thesis on the non-linear feedbacks between soil moisture, vegetation, biogenic volatile organic compounds, gas phase chemistry, and precipitation.


Atmospheric Science, Masters

University of Michigan

I got my Master Degree in atmospheric science studying atmospheric chemistry, planetary atmospheres, fluid dynamics, and computational dynamics.


Meteorology, Bachelors of Science

North Carolina State University

I studied Meteorology with a minor in physics that included thermodynamics, atmospheric dynamics, differential equations, and general and special relativity.


Marine Science, Bachelors of Science

North Carolina State University

I studied Marine Science taking classes in chemical and physical oceanography.

Programming Languages

Bash shell scripting
NCAR Common Language (NCL)

Python Packages


Physical Science Skills

climate and weather modeling
surface-atmosphere interactions
atmospheric chemistry
convective initiation and development
seasonal forecasting
vegetation-atmospheric interactions
biogenic emissions
algorithm development
numerical methods
physical processes
earth system interactions
boundary layer meteorology
high performance computing
time series analysis
geospatial analysis
non-linear system modeling
simple and toy modeling
parameterization development

Data Analysis Skills

machine learning
deep learning
bayesian statistics
data visualization
technical presentation to non-tech audiences
artificial intelligence
bayesian networks
probabilistic graphical modeling
high performance computing
neural networks
data science
concept to code
anomaly detection
dimensionality reduction
unsupervised learning

Platforms & Tools

CI/CD for Gitlab and Github
Amazon Web Services (AWS)
Google Cloud Platform (GCP)
microservice architecture
Command-line interface
Creating interactive dashboards
Web development