Rohil Mogal

• Performed data acquisition, cleansing, and transformation collected from sources like web scraping • Conducted in-depth exploratory data analysis using advanced statistical techniques to identify patterns, trends, and anomalies in equity market data. • Contributed to calculating and interpreting key financial metrics such as moving averages, volatility, and standard deviations to support investment decisions. • Applied Python-based machine learning models for pattern recognition in financial data.

• Extracted, cleaned, and preprocessed large datasets using advanced SQL techniques, ensuring high data integrity and reliability. • Created dashboards in Tableau to monitor key metrics, enabling realtime decision-making and trend analysis. • Performed anomaly detection and trend analysis to uncover business opportunities and optimize performance. • Developed SQL queries to extract and validate data from various sources. • Tools: SQL, Python (Pandas), Tableau.

• Analyzed user engagement data to help optimize product features, leading to increased feature adoption. • Carried out A/B testing on new features, helping improve user retention by 8%. • Analyzed user journeys, identifying key areas for navigation improvements, and increasing user satisfaction.