Vedant Joshi

Walmart Supplier Identification Project: I worked on a client-facing project with Walmart, one of the world’s largest retail corporations. The objective was to identify and evaluate potential suppliers and manufacturers in India to support Walmart’s sourcing and procurement strategy. Key responsibilities included: 1. Categorized and mapped items using SIC and HSN codes in accordance with industry standards. 2. Extracted and analyzed supplier data to ensure alignment with Walmart’s sourcing criteria. 3. Managed and updated category and supplier master data to maintain accuracy and completeness. 4. Handled and maintained vendor-provided data sheets for consistency across datasets. Cross-Functional Collaboration: In parallel with my core responsibilities, I work closely with multiple Business Units across the organization to support and fulfill a variety of requests. Key Contributions include: 1. Providing insights, data support, and process alignment to address BU-specific needs. 2. Acting as a bridge between technical and functional teams to ensure smooth execution of deliverables. 3. Enabling cross-functional collaboration to improve visibility and coordination on shared goals. Generative AI Initiatives: I have been actively involved in spearheading the organization's early Generative AI efforts, focusing on scalable internal adoption and practical applications. Key Responsibilities include: 1. Creating and managing a unified SharePoint repository housing all prompts, accessible organization-wide for easy reuse, collaboration, and knowledge sharing. 2. Designing and developing custom AI prompts using prompt engineering techniques to address a variety of cross-functional use cases. 3. Collaborating with other Business Units to validate, test, and iterate on different versions of prompts to ensure accuracy, relevance, and usability. 4. Fostering a culture of AI experimentation and efficiency through hands-on implementation, internal enablement, and active advocacy.

Worked on multiple projects where I used my skills in Python, Machine Learning, NLP, Power BI, PySpark. Performed Web Scraping, NLP, Machine Learning and Data Cleaning while scraping news related data from multiple news sites. Created a Sentiment Analysis Model, using libraries like NLTK, SpaCy and Transformers which gave 82% accuracy predicting sentiment of news. Implemented TF-IDF Vectorizer and SMOTE Analysis model based on historical data labeling, reducing daily data gathering tasks by up to 20% and minimizing manual intervention. Contributed to the development of a pipeline for parsing complex XML financial data files, resulting in a 75% reduction in manual tasks and increased data compilation efficiency. Conducted TAM (Total Addressable Market) analysis to optimize sales strategies and enhance client acquisition. Utilized PySpark to analyze MSME data, enhancing the integration of recently acquired mobile numbers with existing MSME Data.