Mick Nassar, Data Analysis Developer in Chicago, IL, United States
Mick Nassar

Data Analysis Developer in Chicago, IL, United States

Member since May 19, 2020
Mick is a seasoned developer with experience at a Fortune 100 company creating enterprise back-end API solutions, hands-on machine learning experience in multiple languages, a proven track record of creating products worth millions of dollars, and a patent pending. He loves what he does and he's good at it. Let him put his skills to work for you.
Mick is now available for hire




Chicago, IL, United States



Preferred Environment

Zeppelin, Hadoop, Python, Scala, Spark, Linux, MacOS

The most amazing...

...project I've deployed was called ICC. It's a streaming ML API worth $30 million to Cigna that ensures an insurance claim is billed correctly.


  • Machine Learning Engineer

    2019 - PRESENT
    • Engineered various improvements to my KNN model so the solution can identify insurance claims submitted to the incorrect benefit type with 99.8% accuracy overall in 3-5 seconds per claim.
    • Yielded the company about $30 million in savings every year with the laser-precision of the solution.
    • Created CLI scripts for auto restarting APIs, CLI applications for auto fetching logs from distributed databases, monitoring dashboards with Splunk enterprise, and more as needed.
    • Created design docs and schematics to submit with the company patent application for this product.
    • Began data exploration and modeling for a new NLP use case I pitched to management after the successful launch of ICC.
    Technologies: Data Modeling, Splunk
  • Machine Learning Engineer

    2018 - 2019
    Express Scripts
    • Curated the finest tools, processes, and best practices for development on the organization’s first ML team.
    • Crafted a binary KNN classifier in Scala with Hamming distance from a training set I translated into binary code. This improved processing time so the model could be used with a streaming solution and externalized spring API in real-time.
    • Tested various iterations of my model on different features, training data, and test data with Zeppelin notebook and as a jar on an internal Hadoop cluster to ensure optimal accuracy and that my solution was not overfitting.
    • Launched our production pilot to evaluate the performance of my new KNN model, the communication framework around it, and the accuracy of results. I implemented incremental improvements with feedback from our business stakeholders.
    Technologies: Zeppelin, Hadoop, Java, Scala, Apache Spark
  • Associate Software Engineer

    2017 - 2018
    Express Scripts
    • Created an enterprise API in Java around a legacy product called Oracle Advanced Queue. It facilitates enterprise wide patient eligibility searches, saving about $100,000 dollars every year. It was the biggest value added FY 2017 on the project.
    • Designed and built new Pega applications that automate and manage the lifecycle of a specialty prescription. I got certified by Pegasystems as a systems architect.
    • Solved a difficult problem that arose in an MVP1 production release that was impeding our entire project because users were unable to process payer information. I received recognition from my organization’s VP afterward.
    • Authored many confluence pages to help our engineers, users, and business stakeholders understand our software. I also collaborated with the end-user experience team to improve productivity across the enterprise.
    Technologies: REST API, Oracle, Pega, Java


  • Enterprise Eligibility API (Development)

    I created an enterprise API written in Java around a legacy product called Oracle Advanced Queue. It facilitates enterprise-wide patient eligibility searches to ensure that a patient has active insurance before dispensing specialty prescriptions.

  • Incorrect Claim Center (ICC) (Development)

    I created the ML model for a project called Incorrect Claim Center or ICC for short. Its purpose is to determine if a benefit type on a given insurance claim is correct before being submitted to the payer.

  • Adverse Events (AE) (Development)

    I'm currently creating the model for this ML project. The purpose of the adverse events use case, or AE for short, is to identify a potential adverse event being reported by a patient during a live phone call, document the event for reporting, and transfer them to a qualified clinician to prevent hospitalization or death if possible. This is a use case that I came up with and successfully pitched to management.


  • Languages

    Scala, SQL, Java, Python, JavaScript
  • Frameworks

    Apache Spark, Hadoop, Phoenix, Spark
  • Libraries/APIs

    PySpark, Pandas, Spark Streaming, Scikit-learn, REST API
  • Platforms

    Zeppelin, Apache Kafka, MacOS, Linux, Pega, Oracle
  • Other

    Machine Learning, Data Analysis, Bash Scripting, RESTful APIs, Natural Language Processing (NLP), Progressive Web Applications (PWA), Data Modeling
  • Tools

    PegaRULEs Process Commander (PRPC), Splunk
  • Paradigms

  • Storage

    HBase, Apache Hive


  • Courses in Mathematics
    2018 - 2019
    Harvard Extension School - Cambridge, MA, USA


  • Data Science with Python
  • Certified System Architect
  • Computer Science 101

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