As the primary channel for users to report and resolve service issues, customer care has historically been a critical and resource-intensive aspect of operating cellular networks. However, owing to the inherent complexity in correlating network events with the service performance experienced by individual users, adoption of data-driven solutions leveraging network data for realtime troubleshooting during customer care calls has remained a challenge to cellular service providers (CSPs). In this work, we propose a novel ML aSsisted cEllular sErvice diagNosis (SEEN) solution that infers the cause of user service issues from performance metrics observed from network and assists care agents during customer care calls. Our extensive evaluations demonstrated that SEEN can accurately identify the root cause of user reported performance issues in >80% cases, without relying on information provided by users. Accurate root cause prediction coupled with automated recommended resolution actions implemented in SEEN, lead to significant reduction in handling time to resolve service issues and in trouble tickets volume, improving customer satisfaction and reducing customer care operational expense. Benefit of SEEN is further demonstrated by field deployment in a large CSP.