Description: Our client is currently seeking a DATA SCIENTIST - IV in Irving, TX
Job description:
Responsibilities:
• Support the various AI/ML models that need to be deployed to production
• Troubleshoot various activities and rectify any problems that arise in the end-to-end model pipeline
• Test and validate AI Models. Deploy and manage models throughout the end to end lifecycle.
• Industrialize the model pipeline which includes Testing & validation of the model pipeline
• Be part of the requirements for new enhancements in model/solution
• Propose new ideas/solutions that can be helpful for the project overall
• Analyze and understand problems and issues to convert these insights into system requirements
• Build scalable and high performance Machine Learning and Data Mining algorithms
Irving Manager-part of Verizon business group
Product team manages IOT and SDWAN products and they are the data scientist team
6 total data scientists and adding 2 more headcount once approved/released
More use cases and work load
Input from stakeholder, which include product manager/biz partner, enable the data, develop the models
AI ML statistical models and then do the deployments use and consumption
The requirement is that they should be in Irving, TX
Hybrid-2 days onsite-Everyone is local to TX
Perform data studies and new data sources for new use cases and conducting statistical odeling, experiment design
25% is GenAI building RAGS
Self driven, take 1 use case and come up with solutions and put into production
Someone who has lead projects, self driven, prior experience doing so
Well versed in statistical modeling, data science, genAI, predictive modeling, prescriptive modeling and advanced mathematics
Python-most of their code is weitten
PySpark for scaling the model
SQL-also must have
5 years atleast
Wireless/Wireline/Telecom background
Mostly data from the network 4g/5g/wireline networks
Role duration will be 6 months but will extend as long as they perform through end of 2025
Video interview with Fahad and panel after
Open to relo
Basic knowledge of RAGS, how to build, build knowledge agents etc.
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