At Venus Universal Limited, I analyze options-trading data and have worked with more than 60 million records to identify patterns, segment customers, and generate business insights. This work contributed to a 25% increase in stakeholder engagement.
I built anomaly-detection models for pay-in and pay-out transactions to validate data and flag discrepancies. The work improved suspicious-activity detection by 40% and reduced financial risk by 15%.
I designed and automated more than 15 MIS reports and Power BI dashboards, reducing manual reporting effort by 30%. I also migrated legacy reporting workflows to Looker, cutting report-generation time by 30%.
On my NLP-based client sentiment analytics project, I built sentiment-classification models and pipelines to flag high-priority customer complaints. The project contributed to a 25% reduction in regulatory escalations.
