Ryan Plassl

Created an advanced Natural Language Processing (NLP) model capable of analyzing audio data from call center interactions and gave a score of billing/tech and further expanded tech call into voice audio quality, data connection, texting and further in severity of issue like slow and steadily worst , extreme frustration. The NLP model showcases a multi-faceted skillset, including audio analysis, speech recognition, topic classification, sentiment analysis, and severity assessment

Created an advanced Natural Language Processing (NLP) model capable of analyzing audio data from call center interactions and gave a score of billing/tech and further expanded tech call into voice audio quality, data connection, texting and further in severity of issue like slow and steadily worst , extreme frustration. The NLP model showcases a multi-faceted skillset, including audio analysis, speech recognition, topic classification, sentiment analysis, and severity assessment

The time series analysis of customer experience delta shifts on cell towers involves utilizing pattern recognition on charts and time series data. This analysis helps categorize these shifts into different patterns such as slow burn, single massive spike, or double massive spike. It also identifies abrupt experience changes that have not been resolved. Later provided root cause of dead cells/software changes on cell in same hexagon (Uber library) in geographical area to help guide radio team.