Sonali G
Data & Business Analyst with MBA in Business Analytics (Gold Medalist) and Computer Engineering background
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About
Data & Business Analyst with an MBA in Business Analytics (Gold Medalist) and a Computer Engineering background. 1.5+ years of experience combining technical analytics (SQL, Power BI, Tableau, Python, advanced Excel) with process governance (SOP design, version control, change management) and training/induction delivery. Built an AI-based fraud detection system in Python that prevented an estimated ₹3 crore in losses, presented to the board of directors. Proven ability to not just analyze data but operationalize it — designing the dashboards, the processes, and the training that make insights stick.
Experience
Data Analyst at ElasticRun (2025-09 – Present)
- Own SOP creation and version control, and lead change management for new processes across the ICQA function (current)
- Design and deliver induction and training programs for new and existing joiners (current)
- Conduct monthly PSRs and quality audits across Pune warehouses (current)
- Own the shipment injection project end-to-end (current)
- Built and rolled out training content on the Edurun platform, now the standard onboarding system for new joiners (current)
- Designed and built an AI-based fraud detection system in Python for the Flipkart vertical, helping prevent an estimated ₹3 crore in losses; presented findings to senior management and the board of directors
- Ramped up on Amazon Business operations, shipment lifecycle, and business processes
- Converted to full-time via Pre-Placement Offer (PPO) after internship; owned HR dashboards, headcount reporting, and incentive/gift policy analytics
- Started as Data Analyst Intern in the HR department: built HR dashboards and headcount reports using advanced Excel, and led the launch of a new company-wide gift policy
Summer Intern at Kalyani Maxion Wheels (2024-06 – 2024-08)
- Built a Non-Conformance Management System with Excel-based dashboards to track wheel rejections
- Analyzed rejection data to identify root causes, improving visibility into quality issues
