Senior Data And Applied Scientist
Current* Query Slot Detection for Microsoft Enterprise Copilot (M365 Chat)Led the development of an ML model for intent and slot detection in Microsoft Enterprise Copilot queries. Managed the entire development cycle, from ideation and data curation to model training and deployment. The model has shown significant potential in improving the relevance metrics for the query entity suggestion service in Copilot.*Learned Ranker Models for Enterprise Entity SuggestionsDeveloped and deployed ranking models for entity suggestions (Emails, Files, Keywords, etc.) in query formulation for Microsoft enterprise solutions like Outlook and Teams. These models significantly enhanced customer engagement metrics, such as time-to-success, driving increased revenue for the company.* Document Attachment Suggestions in OutlookDeveloped a semantic similarity model to improve document recommendations in Outlook's Suggested Attachments feature. Leveraged Turing-based architecture to estimate contextual relevance, integrating the similarity index into the ranking model. This approach led to significant improvements in ranking metrics.
