Nitin Bhardwaj

Objective Approach : HR Automation :

Current Project : HR LLM Chat Bot & HR Automation

LLM Chat Bot :

• Task Summary : Developed Employee Query Assistant leveraging Python and Streamlit, integrating OpenAI API for advanced natural language processing. Implemented RAG-based PDF content analysis for precise query resolution, enhancing productivity and insights..
• Impact :This project significantly improves query resolution, user experience, productivity, knowledge management, scalability, and data-driven insights through RAG-based PDF content analysis.

HR Automation :

• Building end-to-end pipeline for HR automation, streamlining downloading and shortlisting processes. Creating pipeline to store HR data in database and integrating it with Power BI for reporting and analytics.

Previous Project 1 : SAP BO report migration to Power BI

• Led transformative migration of reporting systems from Business Objects to Power BI, unlocking dynamic insights and overcoming past limitations.
• Spearheaded transition, introducing interactive dashboards, rich visuals, and seamless data integration.
• Made analytics better by combining advanced features with easy-to-understand tools, which improved organization reporting
• Technologies used: Power BI , SQL , Power Automate.

Previous Project 2 : Worked as Analyst

• Enhanced daily number reporting procedures to ensure accuracy, efficiency, and clarity in conveying essential business metrics
• Responding promptly to ad-hoc requests from various teams
• Conducting testing on website and mobile application event data
• Technologies used: Adobe Analytics ,Charles Debugger, SQL , Powe rBI

ML models predict clients for future & option segment activation and buy now, pay later services, effectively targeting those with suitable behaviors for engagement.

• Impact : F&O prediction model achieved outstanding accuracy for the future and option segment. Out of every 10 identified potential candidates, we successfully converted 3 clients, signifying a significant increase in segment activation.
• Our buy now, pay later model significantly boosted revenue by 23% by accurately identifying leads likely to use this service, enabling precise targeting and engagement.

Sentiment analysis project by harnessing Python libraries to extract reviews from Google Play Store. Our goal was to gain insights into customer opinions by identifying specific topics within each review and evaluating overall sentiment associated with these topics.

• Approach :We preprocessed reviews using Lemmatization and Tokenization techniques for analysis. Then, we categorized reviews into topics using regular expressions, assessed sentiment with a sentiment analyzer library, and validated accuracy by cross-referencing scores with predefined labels.
• Impact : Our approach identified areas for improvement by pinpointing topics with negative sentiment, leading to a deeper understanding of customer feedback and uncovering opportunities for product/service enhancement.

Developed web scraping : Script designed to streamline data acquisition process for our sales team's insurance sales planning efforts across India. Leveraging Selenium Web Driver, our Python script interacts with web elements, automating entire data retrieval process.

• Summary : Managed cross-team data requests, analyzed customer behavior through Hive queries, and developed dashboards to track website and app metrics. Created technical spec docs for event tracking sheet, detailing requirements and functionalities.
• Technologies used: Hive, HQL , Power BI , MS-Excel
• Contributions: Daily Reporting , Stakeholder Requirement