Himanshu Kumar

Business Analyst with 1.8+ years of experience in data analysis, requirement gathering, and process optimization in the financial services domain. Skilled in Python, SQL, and translating business needs into actionable insights to support decision-making. Strong analytical mindset with experience in data-driven problem solving and cross-functional collaboration.

Business Analyst - Money Honey Financial Services

(2024-10)

• Redesigned the email tracking system by replacing a 30-minute batch scheduler with real-time event logging, enabling instant engagement analysis and reducing reporting latency by 100%
• Integrated Amazon SES with a custom webhook to capture and store real-time user activity data, powering downstream analytics and KPI tracking, and providing marketing teams faster optimization insights
• Designed and implemented a UCC-based investment system for FD products, enabling one-time data capture and reusable investor profiles, reducing transaction processing time by 40% and improving user experience
• Conducted UAT testing of Bajaj Finance Fixed Deposit APIs to validate data accuracy and transaction scenarios, and designed end-to-end user workflows in Figma aligned with API behavior, covering KYC integration and payment processes
• Created functional documentation, including data flows and field-level requirements, and collaborated with developers to ensure accurate implementation and data consistency across systems

Deep Learning Engineer Intern - Smarttrak AI

(2024-01 - 2024-10)

• Developed an intelligent blueprint query system using OpenCV, Gemini, and GPT models to extract and query structural data from house blueprint PDFs; enabled natural language querying and vector database integration for fast, accurate retrieval
• Cleaned and analyzed over 100k+ time-series records from solar inverters, improving data quality by 95% and enabling accurate performance monitoring across multiple solar energy projects
• Built automated Python pipelines to track inverter performance trends, detect anomalies, and reduce manual reporting time by 30%, supporting faster data-driven decision-making for stakeholder
• Designed dashboards and detailed reports highlighting fault patterns and predictive maintenance opportunities, enabling the operations team to take proactive actions and reduce costly downtime by 15%

Earth Science - Indian Institute of Technology, Kanpur (2020 - 2024)