Udaykumar Gajavalli

Designed and deployed Generative AI workflows using Azure OpenAI with sales playbooks and CRM data, reducing proposal turnaround time by 60% and driving a 15% increase in deal conversion rates.

Built an AI-Driven Deal Desk Consultation Optimization tool, integrating LLM, Airflow, and Trino SQL to reduce manual analysis time by 80% and accelerate deal consultation efficiency across global teams.

Delivered personalized GenAI-powered email recommendations through cluster-based behavioral segmentation, achieving a 28% uplift in product usage and up to 95% feature adoption in pilot campaigns.

Partnered with cross-functional teams to coordinate deployments to 250K+ users across SMB, Mid-Market, and Staffing, while designing experimental frameworks and KPIs to measure adoption, product usage, and churn impact.

Developed and productionized a sentiment analysis pipeline leveraging RoBERTa models, improving classification accuracy by 25% and automating monthly reporting, reducing manual effort by 40%.

Built ML models for wallet share prediction, improving customer profiling and enabling targeted marketing strategies to drive revenue growth.

Delivered 85% accurate demand & P&L forecasts, effectively supporting inventory optimization and revenue planning for key clients.

Analyzed promotions & loyalty programs for Costa Coffee, driving critical ROI insights, achieving churn reduction, and optimizing pricing strategies.

Applied advanced segmentation, CLTV, and price elasticity modeling techniques, boosting promotional effectiveness and increasing customer engagement across campaigns.

Developed an anomaly detection package (Z-Score, IForest, HBOS) in Azure Databricks, empowering store managers to identify irregular sales patterns and make data-driven decisions across regions and categories.

Developed ML models for churn and risk prediction, enabling proactive customer retention strategies and reducing potential revenue loss.

Built a transaction volume forecasting model using FbProphet, improving capacity planning and optimizing TPF resource allocation for authorization requests.

Implemented robust data preprocessing and feature engineering pipelines to enhance model accuracy and reliability across various projects.

Partnered with client stakeholders to align forecasting insights with operational efficiency and risk management goals, ensuring strategic business impact.