Bank of America, Gandhinagar
TECHNICAL ANALYST II (Data Analytics Focused) | July 2022 – Present

 Built classification models using logistic regression, Random Forest, and XGBoost to predict credit risk with accuracy of 95%.
 Optimized complex SQL queries by restructuring joins and adding indexes, reducing report runtime from 18 minutes to under 2 minutes.
 Reduced duplicate customer IDs by 42% through entity resolution and survivorship rules, boosting campaign targeting accuracy.
 Increased validity of contact attributes (email, phone) by 18% by applying cleansing rules, reducing bounce rates in marketing campaigns.
 Documented 50+ key business rules, data flows, and exceptions, ensuring audit readiness and smoother onboarding for new analysts.
 Improved discoverability of critical datasets, reducing time-to-insight for analysts from 3 days to 4 hours.
 Translated complex root-cause analyses into 5-slide executive narratives, influencing process changes that reduced defects by 25%.
 Delivered actionable recommendations from diagnostic analysis that helped increase customer satisfaction (CSAT) scores by 12%.
 Conducted descriptive and diagnostic analytics on IVR and customer interaction data to uncover patterns, improve call routing efficiency, and reduce defect rates by 15%.
 Designed and automated 10+ interactive dashboards (Tableau/Power BI), reducing reporting turnaround time by 40% and enabling leadership to track KPIs (ROI, churn, adoption, NPS) in real time.
 Implemented row-level security (RLS) in Power BI, ensuring secure access across 3 role types, driving 100% compliance in reporting.
 Drove 12% uplift in campaign CTR by translating business goals into measurable KPIs, designing A/B tests, and delivering actionable insights to marketing and product teams.
 Partnered cross-functionally with Product, Marketing, and Finance to analyze adoption and engagement data, uncovering $1M in annual upsell opportunities through customer health scoring models.
 Conducted root cause analysis of customer support escalations, identifying 3 systemic process gaps and implementing fixes that reduced SLA breaches by 25%.
 Developed SQL-based ETL workflows to prepare and validate multi-million record datasets, cutting manual data prep time by 60% and ensuring 100% compliance with audit requirements.
 Created predictive usage and retention models using Python (Pandas, Scikit-learn), achieving 80%+ accuracy in identifying at-risk accounts and helping reduce churn by 15% YoY.
 Delivered monthly executive performance decks blending data visualization and narrative storytelling, influencing strategic decisions on customer journey redesign and marketing spend allocation.
 Automated 5+ recurring reports and manual workflows with SQL and Python, saving ~200 analyst hours annually and improving data reliability across teams.