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Zach Boldyga, JavaScript Developer in Seattle, WA, United States
Zach Boldyga

JavaScript Developer in Seattle, WA, United States

Member since February 28, 2017
Zach’s abilities are best captured through his recent, five-year founding and management of Scalabull, a software company that currently powers over 1 million patient lab testing transactions in the US annually. He’s gradually stepping away from the organization over the course of 2018 and looking forward to an exciting transition into the applications of AI in the arts and entertainment!
Zach is now available for hire

Portfolio

  • Scalabull
    Python, Jupyter Notebook, TensorFlow, Scikit-learn, Node.js, Mongo...

Experience

  • JavaScript, 5 years
  • MongoDB, 4 years
  • Python 3, 3 years
  • TensorFlow, 1 year
  • Scikit-learn, 1 year
Seattle, WA, United States

Availability

Part-time

Preferred Environment

*nix, JavaScript or Python, TensorFlow, MongoDB

The most amazing...

...thing I've done was to found my own company, Scalabull.

Employment

  • Founder | CTO

    2013 - PRESENT
    Scalabull
    • Architected and managed a technology stack with over 150 microservices; implemented over 100 of the microservices.
    • Built a platform for submitting and receiving medical data to/from hospitals, physician's offices, and laboratories in a variety of formats—resulting in a guaranteed real-time message delivery.
    • Leveraged millions of healthcare datapoints to implement machine learning tools that optimize workflows and provide non-trivial insights.
    • Ensured that HIPAA security best-practices are used throughout the organization.
    • Managed more than team projects.
    • Adjusted the organization to measure and improve our Net Promoter Score.
    Technologies: Python, Jupyter Notebook, TensorFlow, Scikit-learn, Node.js, Mongo, Elasticsearch, Redis, NSQ.io, ZeroMQ, Meteor, React, Ruby, CouchDB, MySQL

Experience

  • 100+ Scalabull Applications (Development)

    I architected, built, and performed DevOps for over 100 of the microservices that power Scalabull.

    Includes:
    • User applications run in Meteor and React.
    • Analytics dashboards built with React and Chart.js
    • REST APIs
    • Generation of a variety of clinically-approved PDF formats with conditionally changing layouts and barcodes.
    • Integration into SOAP web services provided by Quest Diagnostics and LabCorp.
    • Created more than 1,000 HL7 interfaces.
    • MLLP server and client.
    • In-house SFTP client.
    • Resilient databases for ~50 tables/collections. Historically Scalabull's data-at-rest has resided in MySQL, CouchDB, and MongoDB.
    • Supplementary machine learning tools.
    • ZeroMQ, Redis, and NSQ used for various messaging and key/value store needs.
    • Elasticsearch for information discovery.
    • Nagios to track system failures across over 40 metrics and provide real-time alerts to developers. Annual uptime of over 99.9%.
    • Fault tolerant infrastructure with combined hardware firewalls, dedicated servers, and cloud servers. Load balancing, PM2, and Nohup for process management and clustering; Docker and Rackspace images for easy replicability.

  • Predicted Delivery Times for Lab Results (Development)

    US lab results contain a number of timestamps (always including the original time the test was ordered, and the time the testing was completed and when the result was sent to the patient). Using millions of these examples, I created a relatively simple, multi-layer neural network that predicts when ordered tests will be ready to be sent to the patients.

    If a physician orders a TSH panel from Quest Diagnostics—due to the fact that we've seen over 50,000 of those transactions—our system estimates how long it will likely be before the result is returned. While relying on deep learning allowed us to avoid complex feature extraction in this implementation, the initial implementation used multivariate linear regression and required feature extraction. Therefore, the neural network relies on information like the day of the week and time of day the test was ordered, the type of test, the age of the patient, and the lab and/or region of the US.

    Estimates are accurate about the day and time of day (morning, mid-day, afternoon, or evening) over 95% of the time.

    This is currently considered proprietary so the code is not included.

  • React JSON-schema Form Ecosystem (Development)
    https://github.com/RxNT/react-jsonschema-form-conditionals

    I orchestrated and contributed to the development of multiple open source extensions to the react-jsonschema-form, a utility for creating user input forms from a JSON schema object on-the-fly. Our plugins allow users to significantly enhance this tool and create enterprise-grade forms from JSON-based configurations.

    The projects include:
    • Extras: an assortment of fancy widgets (e.g., typehead text fields).
    https://github.com/RxNT/react-jsonschema-form-extras
    • Pagination: multipage forms are supported.
    https://github.com/RxNT/react-jsonschema-form-pagination
    • Conditionals: a rules engine can be applied to compute values in the form, hide form fields, and manipulate form CSS.
    https://github.com/RxNT/react-jsonschema-form-conditionals
    • Form Manager: This makes it simpler to plug all of our utilities together into an enterprise-grade application.
    https://github.com/RxNT/react-jsonschema-form-manager

  • Redis-based Stream Deduplicator (Development)

    Applications in banking, telecom, and healthcare sometimes send and receive data in a message-based format that leads to frequent duplicates at the application layer.

    This stream deduplicator can process up to 50,000 messages per second and filter out any duplicates. This was built for a need to prevent duplicate patient lab-result messages and is battle-hardened via production use at Scalabull.

    This code is fairly old, but it demonstrates some understanding of how Node.js works under the hood and how V8 works.

Skills

  • Languages

    JavaScript, Python 3, Ruby
  • Paradigms

    Functional Programming, Object-oriented Programming (OOP), Gang of Four (GOF) Design Patterns
  • Platforms

    Jupyter Notebook, Linux, Rackspace, Meteor, *nix
  • Libraries/APIs

    Scikit-learn, NumPy, TensorFlow, React
  • Storage

    CouchDB, MongoDB, Redis, MySQL
  • Other

    Nohup

Education

  • Completed the program in Alternative MBA Studies
    2018 - 2018
    altMBA - www.altmba.com
  • Completed 6 months of project-oriented courses in Deep Learning
    2017 - 2018
    Fast.ai, DeepLearning.ai - Online
  • Bachelor of Science degree in Computer Science
    2008 - 2011
    University of Maryland, College Park - College Park, MD, USA
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