Verizon is one of the world’s leading providers of technology and communications services, transforming the way we connect across the globe. We’re a diverse network of people driven by our shared ambition to shape a better future. Here, we have the ability to learn and grow at the speed of technology, and the space to create within every role. Together, we are moving the world forward – and you can too. Dream it. Build it. Do it here.
What you’ll be doing...
SMO Analytics team is a part of the Verizon Service Now Team. Our Goal is to reduce the number of contacts (Incidents, Calls or Chats) to Service Desk, look at the ways to promote self-help by Leverage AI\ML Algorithms and Technologies. We also work with Desktop Engineering & Planning team on AIOPs Solutions to identify the issues proactively, event correlation and RCA. Our North Star is the employee experience. To provide the insights needed to fulfill this goal, we have core teams focused on Analytics, Data Visualization, Data Engineering and RPA. As part of emerging tech you will be working on various tech stacks related to AI/ML, Public Cloud Tools like Sagemaker, Comprehend, Lambda etc. along with in platform analytics tools like Splunk, Servicenow, Moogsoft etc.
Drive user stories and end-end engagement of analytics and feedback loop measurement partnering with Stakeholders and Internal Teams.
Generate Insights Using AI\ML and Data Science at its core.
Build Solutions using Machine Learning as well as Deep Learning and NLP.
Debug and Improve the Model Performance.
Manage and Analyze data in order to build high data driven business insights and high impact models to generate significant value.
Convert Data Insights to ML Solutions.
Handle the projects Independently End to End.
Work independently and collaboratively across various teams.
Where you'll be working:
This hybrid role will have a defined work location that includes work from home and assigned office days as set by the manager.
What we’re looking for...
You’ll need to have:
Bachelor's degree or one or more years of work experience.
Experience in Data Science Domain, applying AI/ML principles to real world applications.
Strong Fundamentals in Probabilities, Statistical Data Analysis.
Strong ML Experience - Exploratory Data Analysis, Model Selection, Hyper Parameter Tuning.
Experience working on Python - Pandas, Numpy, Scikit-Learn, NLTK.
Experience on any of the visualization tools like Qlik/Tableau.
Knowledge of Deep Learning (Keras, Tensorflow) or Time Series.
Experience on ITSD Domain, Public Cloud like AWS.
Even better if you have:
Two or more years of relevant work experience.
Basic knowledge of Big Data Ecosytem like Spark, Hadoop etc.
Teamwork, Problem-solving, good written and verbal communication skills.