Roderick H.

Roderick h.

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location of Roderick H.Denver Metropolitan Area
Phone number of Roderick H.+91 xxxx xxxxx
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  • Timeline

    Aug 2019 - Mar 2021

    Security Officer

    HSS
    May 2021 - Aug 2021

    Software Engineer Intern

    Upwork
    Oct 2021 - Sept 2022

    Software Engineer

    Kite
    Jul 2022 - Feb 2023

    Stock Forecasting App

    Open Source
    Current Company
    Oct 2022 - now

    Logistics Analyst

    Denver Last Mile Logistics
  • About me

    Software Engineer

  • Education

    • Metropolitan state university of denver

      2016 - 2021
      Bachelor of applied science - basc aviation and aerospace 3.2 gpa

      - IFR and VFR Flight planning/flying with single/twin engine aircraft- Deep Learning: Recurrent, Convolutional & Neural networks. LSTM's. Adam, Dropout and Initialization.

  • Experience

    • Hss

      Aug 2019 - Mar 2021
      Security officer

      - Attention to detail, situational awareness and active listening. - Ability to solve problems under pressure. - Technical knowledge of security systems.

    • Upwork

      May 2021 - Aug 2021
      Software engineer intern

      - Worked closely with engineers from multiple disciplines to test software systems.- Improved efficiency to diagnose EC2 network tickets by 70–80% through automated detection of networking issues

    • Kite

      Oct 2021 - Sept 2022
      Software engineer

      - Built machine learning models for critical ads-related entities to power ads targeting product in selection funnel or place into ranking funnel as features- Reduced customer friction for 90%+ of customers by making semantic similarity model inference latency 16x faster

    • Open source

      Jul 2022 - Feb 2023
      Stock forecasting app

      Designed and deployed Stock Forecasting application. Shows current data of given stock between 2 dates. Given forecasted length, app will output estimated rises/falls over given days

    • Denver last mile logistics

      Oct 2022 - now
      Logistics analyst

      - Redesigned data pipeline architecture for existing project that handles 20% more routes - Collaborated with applied scientists to build an ensemble of ML models, and enable ensemble real time serving by saving base model predictions in feature store (Redis). Resulting in a 15% improvement accuracy, and approximately 30% of new data being eligible for automated predictions

  • Licenses & Certifications