• Led team of data scientists and machine learning engineers in improving the QuickBooks New User Experience through adoption of Generative AI technologies and applications of Intuit’s existing machine learning capabilities• Defined team’s quarterly roadmaps in partnership with product and engineering leaders• Developed AI Planner to orchestrate specialized AI agents in response to customer queries• Built extensible framework and recommendation models for generating Business Insights for customers based on their business and accounting records (recognized with Intuit’s 2023 Scott Cook Innovation Award for this work)• Collaborated with university researchers at UT Austin on developing cutting edge methodologies for Hierarchical Time Series Forecasting and their applications to reconciling cash flow forecasts at various levels• Invented patent-pending approaches for robustly generating insights for customers via clustering and performance analysis

• Implemented Python based contextual bandit models to algorithmically select items for quality assurance inspection• Designed real-time/online experiments validating dollar value lift of the contextual bandit QA model• Secured allocation of engineering and computational resources across team lines required to drive project’s success

• Optimized company’s B2B lead scoring platform through research and creative application of machine learning methods• Created tools, applications, and Spark SQL pipelines to automate data processing, feature selection, and model evaluation• Drove new client acquisition via superior performance on Proof-Of-Concept (POC) projects and client-facing presentations

• Built customizable models to optimize both C-curved diminishing ROIs and S-shaped logistic ROI’s • Enhanced allocation of marketing mix elements across media channels and product categories through algorithmic optimization• Created combinatorial model to optimize decisions on product line rationalizations