NEELABH PANT

• Lead a proficient team dedicated to leveraging cutting-edge GenAI capabilities to revolutionize finance operations at Walmart.
• Focus on developing and deploying advanced models, fostering innovation and collaboration to uphold industry-leading standards.
• Empower team to deliver impactful solutions, driving operational efficiency and elevating customer experiences across Walmart's ecosystem.

• Developed and deployed advanced time series forecasting models for business operations, including an anomaly detection system for Walmart's financial data.
• Lead technical front of GenAI team, creating cutting-edge NLP solutions using diverse Language Model architectures such as GPT, Llama, and Google Bard Gemini.
• Address complex challenges in legal and finance domains, leveraging techniques like fine-tuning pre-trained models and adopting Retrieval Augmented Generation (RAG) approaches.

• Developed and deployed advanced time series models for forecasting and analyzing Walmart's financial data for business consumption.
• Constructed Business Intelligence (BI) applications using tools like PowerBI and Tableau to enhance analysis and deployment of model outputs.
• Published papers and delivered talks at various research conferences and symposiums.
• Formulated and managed data-driven projects, designing robust architectures, and leading teams to implement multiple functionalities within these structures.
• Collated and cleaned data for use by other data scientists, consistently monitoring performance and providing practical guidance to foster a high-performance data science environment.

• Identify and solve domain-specific problems using Machine Learning and Data Science; extract, clean, and scrub data sets (60-70% of the time).
• Build and benchmark Time Series, Computer Vision, NLP, and predictive models; analyze data sets and determine optimal models for production.
• Collaborate with data engineers to define and implement processes for model deployment and consumption.

• Analyzed customer data to identify behavior and purchase patterns, and used GIS to determine spatial influences on customer issues.
• Extracted customer sentiments from textual data and identified needs, targeting the most profitable customers with efficient salesmen based on historical behavior.
• Assigned customer scores using multivariate and univariate models, and set up sales routes using traditional routing methods.

Delivering C programming lectures, providing hands-on class experience, offering individual office hours for student discussions, and grading assignments, projects, and exams.