Senior Data Scientist
Red Hat Inc. (subsidiary of IBM)
Jaydeep Rane is a Senior Data Scientist on the Product Data Science team at Red Hat. He has been instrumental in implementing multiple Machine Learning projects that have directly impacted the revenue of the company. His expertise lies predominantly in the fields of Natural Language Processing (NLP) and Predictive Analytics which he used for building models that enhance user experience on the customer's query portal, predict cross-sell/up-sell opportunities and identify Independent Software Vendors (ISVs) using a purely data driven approach. Jaydeep completed his Masters in Computer Science with a specialization in Data Science from North Carolina State University and has been associated with Red Hat for over 3 years. He has won international Gold medals for his country in Swimming and prefers escaping the city for hikes and other outdoor adventures.
Jaydeep Rane is speaking at the following session/s
Optimizing Customer Self-Solve Experience Using Intent at Red Hat
Self-solve experience is not optional but the new default. Customers subscribe to Red Hat products with varying levels of operational complexity. Ensuring customers have the right resources such as knowledge base, tooling, and support for troubleshooting is the key to customer renewal. Self-solve options save time and reduce customer frustration. Automation around top problems, proactive diagnostics collection, support request routing are the ways to improve workflow efficiency of support personnel handling the increased case workload.
In this session, we will present challenges and techniques used to improve the findability in self-solve experience and patterns to improve the workflow efficiency. We will also cover the need to infer customer intent from search keywords. We will present a way to differentiate the search intents using machine learning and the steps involved in it such as problem framing, data collection, model building, and end to end integration.
Beginner to Intermediate skilled Data Scientists with an interest in building text-classification models using natural LAnguage Processing. Experience coding in Python is a pre-requisite.
Learn how to leverage these patterns to promote the self-solve experience of their site, and walk away with key lessons in building and deploying an end to end machine learning system in production.