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Darron Fuller, Data Analytics Developer in Washington, DC, United States
Darron Fuller

Data Analytics Developer in Washington, DC, United States

Member since May 21, 2019
Darron has 15 years experience as a client-facing lead engineer of infrastructure and mission-critical advanced analytics software for notable clients in the renewable energy industry and U.S. Department of Defense. He has degrees in data analytics and computer science, covering machine learning and decision optimization.
Darron is now available for hire

Portfolio

Experience

  • Data Analytics, 20 years
  • Object-oriented Programming (OOP), 20 years
  • NumPy, 7 years
  • Pandas, 7 years
  • Scikit-learn, 7 years
  • Machine Learning, 7 years
  • Python, 7 years
  • PyCharm, 7 years
Washington, DC, United States

Availability

Part-time

Preferred Environment

Python, PyCharm, JupyterLab, Docker, AWS, K8s, Git

The most amazing...

...project I've designed was an advanced energy market trading algorithm with multiple machine learning (predictive) and optimization (prescriptive) models.

Employment

  • Lead Machine Learning Engineer

    2013 - PRESENT
    StreamEdge Analytics, LLC.
    • Responsible for the design and implementation of specific components of the Xfinity Voice Assistant Platform that is used by millions of customers every day. Lead engineer on Comcast Labs Applied AI research and engineering team that owns the full stack and operationalization of voice product offerings, as well as advanced analytics and the machine learning pipeline work. Used advanced machine learning and Cloud technologies to process the billions of natural language queries received every year, and to make a user’s input actionable. (http://dclabs.comcast.com).
    • Supported the $1.2 billion contract with Centers for Medicare Services and worked as the lead machine learning engineer on the project. I focused on predictive modeling, machine learning, and data mining on a large healthcare dataset. SERCO-NA (https://www.serco-na.com).
    • Developed predictive models to improve the effectiveness of child support collection activities and monitoring of the health and safety of children in foster care. I oversaw the project as the lead machine learning engineer. Information Builders (ibi.com).
    • Developed multi-terabyte Automated Vehicle Location data sets consisting of the operational details of public transportation systems for major metropolitan transit systems as the lead big data analytics engineer. I applied advanced data mining, statistical inference, and machine learning techniques to identify unsafe operational practices in public transportation systems.
    • Supported the special activities for the Chairman of the Joint Chiefs of Staff, and Office of Secretary of Defense, and Defense Manpower Data Center. I engineered and managed mission-critical manpower analytics and logistics systems.
    • Collaborated as the teaching and technical assistant to the lead instructors for the Predictive Analytics for Healthcare workshop of the largest conference on Predictive Analytics.
    Technologies: Scikit-learn, Keras, TensorFlow, AWS, GCP, Git, GitHub, Python, Pandas, Jupyter, NVIDIA GPU
  • Machine Learning Software Engineer

    2017 - 2018
    Greensmith Energy (Acquired by Wärtsilä)
    • Developed the machine learning and analytics software for the simulation and optimized operation of advanced renewable-energy storage systems. (ref: https://singularityhub.com/2019/07/21/machine-learning-vs-climate-change-ai-for-the-greener-good/).
    • Designed and developed energy market trading optimization algorithms and applications.
    • Designed and developed Greensmith Energy's first real-time energy market trading process that makes use of advanced optimization models. Ran the models through a 30-day operational test with Électricité de France, the world's largest producer of electricity. (https://en.wikipedia.org/wiki/Électricité_de_France).
    • Designed and developed an energy analytics framework, including energy management optimization, machine learning-based forecasting, and simulation services.
    • Proposed and led design, acquisition and, implementation of Greensmith Energy's largest software acquisition for embedded enterprise and reseller licenses of Gurobi Optimization and Mathematical Solver into Greensmith's Energy Management and Control System (GEMS), CD/CI pipeline, and GEMS Cloud and remote customer deployments.
    Technologies: Python, Scikit-learn, Pandas, Numpy, Gurobi Optimizer
  • Senior Software Engineer, Business Intelligence Specialist

    2005 - 2013
    Independent Consultant
    • Developed for various federal, DoD, intelligence communities, and Fortune 500 companies as an enterprise software engineer and business intelligence consultant.
    • Provided expertise on a variety of data extraction methods that would facilitate the State of New York's compliance with Hearst's FOIA request. I was the expert technical witness for Hearst Publishing in Hearst vs. State of New York (https://casetext.com/case/hearst-v-state-of-ny).
    Technologies: Java, Python, Cognos BI, Tableau

Experience

  • Active Learning Research: Department of Computer Sciences at George Mason University (Development)

    Active learning is a special case of machine learning in which a learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points. In statistics literature, it is sometimes also called optimal experimental design. [Wikipedia: https://en.wikipedia.org/wiki/Active_learning_(machine_learning)]

  • Renewable Energy Market Trading Optimization (Development)

    Designed and developed Greensmith Energy's first real-time energy market trading process that makes use of advanced optimization models. Successful 30-day operational test by the world's largest producer of electricity, Électricité de France (EDF: https://en.wikipedia.org/wiki/Électricité_de_France) in a CAISO five-minute real-time energy market simulator. [https://en.wikipedia.org/wiki/California_Independent_System_Operator]

Skills

  • Languages

    Python, Java, C++
  • Paradigms

    Linear Programming, Agile, Object-oriented Programming (OOP), SOLID Principles, Functional Programming
  • Other

    Machine Learning, Amazon Machine Learning, Google Cloud Machine Learning, Predictive Analytics, Predictive Modeling, Optimization, Mixed Integer Linear Programming, Data Analytics, Model Validation, Kubeflow (Containerized ML Orchestration on Kubernetes), Bokeh
  • Libraries/APIs

    Scikit-learn, Pandas, NumPy, Keras, TensorFlow
  • Tools

    Gurobi, PyCharm, IntelliJ IDEA, CPLEX
  • Frameworks

    MOA

Education

  • Master of Science degree in Data Analytics Engineering
    2014 - 2017
    George Mason University - Fairfax County, Virginia, USA
  • Graduate Certificate in Predictive Analytics
    2014 - 2015
    University of California at Irvine - Irvine, California, USA
  • Bachelor of Science degree in Computer Science
    1987 - 1993
    University of Maryland at College Park - College Park, Maryland, USA
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