Software Tester 3 – Janesville, WI

  • #3640949
    Mark Kim 72.***.223.102 611

    MOA

    Job posting expires at 2021-10-25

    Please see job description below and send your resume at hr@moalink.com if you are interested in the position.

    Job Overview
    Looking for a detail-oriented, that has technical background in telecommunication that can maximize multitasking skills to the next level.
    Individual(s) will make/coordinate project’s schedule and create test plan as well as report to HQ or internal team of the risk points before milestones.
    Working independently, translation of complex functional & technical requirement is required, maintaining upcoming technical knowledge is a must, and must be able to diagnose protocol/system issues
    For a successful career in MQL, one must show all above abilities and constantly strive to be better by self development and communications.”

    Education/Experience:
    � Bachelor’s degree in business management, economics, finance, accounting or relevant field required.

    Essential Duties & Responsibilities ”

    – Coordinate with R&D to implement new standards and specification related changes and/or carrier specific requirements
    – Use technical engineering knowledge and experience to understand customer applications, test cases, and requirements. Interworking capability with teams in different time zones.
    – Works independently & determines approach to work, and is monitored/supervised based more on milestone basis, and at key deliverable junctures
    – Identify the required documentation and process for the project. Assist in creation of design requirements. Assist in analyzing test data and reports to determine if design meets functional and performance specifications. Collaborate with other engineering staff to evaluate interface between hardware and software products. Analyze test data and reports to determine if design meets functional and performance specifications
    – Diagnose Protocol and System level issues from the carriers, screen through and debug issues reported from carrier, and oversee IOT and field test by doing in-depth log analysis”

    “Qualification (Background and Experience)” ”

    Bachelor’s degree in Engineering, preferably in Computer Science, Computer Engineering, and Electrical Engineering or equivalent level of work experience
    2+ years of experience in wireless industry preferably in Mobile QA or Mobile testing
    Familiar and Hands-on experience of wireless standards/protocols preferably GSM/CDMA/WCDMA/LTE/5G and Radio interface and/or exposure to RF network engineering
    Good knowledge in LTE features including VoLTE/ViLTE/VoWiFi/RCS messages/OTA Activation
    Good knowledge of wireless technologies such as GSM and CDMA is required, and also familiar with 5G technologies a plus including but not limited to mmWave/Sub6 and Beamforming.
    Experience in utilizing UE log post processing tools (QXDM, QCAT, QPST, QMICM, NNEXT, SHANNON DM, N-Trace, Mobile Analyzer, Artemis, Debug Mux, Spy Tracer, Odin, XCAL, Mobile Datum, PC Datum, Wire shark) with good understanding in OTA signaling messages.
    Able to test in driving condition, and able to travel at any given moment
    Strong problem solving and analytical skills, communication(verbal and written) skills, and organizational and documentation skills
    – Core competencies in Java and/or C#, also Familiarity with scripting languages such as Python to Write Test automation code using Eclipse, Android Studio, Visual Studio.
    – Performs work under time schedules and stress which are normally periodic or cyclical, including time sensitive deadlines, intellectual challenge, some language barriers, and project management deadlines
    – Must have a driver license and a good driving record

    • Bella Joha 202.***.197.184

      Machine learning projects require not only strong data science expertise but also solid engineering practices to ensure scalability. While researching vendors, I reviewed https://acropolium.com/services/machine-learning-development/ and appreciated how clearly they outline their ML implementation stages. From data preparation to deployment and maintenance, the structured approach suggests real-world experience. In my opinion, successful ML initiatives depend heavily on this kind of disciplined process and cross-functional collaboration.

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