Resume

Relevant modules: Statistical Software for Data Management and Decision Making (Python), Data Visualization, Econometrics, Regression, R Programming, etc. Top 5% of my class. Member, The Honors Society of Phi Kappa Phi.

Equipped with skills and knowledge to apply data analytics techniques to solve complex business problems.

Graduated top 10 percent in the class.

Designed the ETL pipeline, wrote SQL queries to monitor Alexa's friction rates, and built a Tableau dashboard to enable key metric tracking. Analyzed user interaction data with Alexa Assistant, focusing on usage patterns, command types, user preferences, and engagement metrics. Identified trends and insights related to Alexa usage, feature adoption, and user behavior. Created weekly and monthly business reports and dashboards that tracked these KPIs, providing actionable insights to stakeholders. Monitored and reported on the adoption rates of new features and updates in the Alexa Assistant. Conducted cohort analysis to understand how different user segments interacted with various features. Led a research and development initiative that facilitated the team's migration from a legacy BI tool to a newer, more capable one with enhanced self-service features. Drove the successful implementation of machine learning-powered anomaly detection and natural language query (NLQ) functionalities into a QuickSight dashboard, empowering stakeholders to make data-driven decisions with heightened precision. Presented data findings and recommendations to stakeholders and higher management.

Managed business requirements, and project planning, and addressed technical challenges in delivering analytics projects for the loan application, focusing on user behavior, loan application trends, and repayment metrics. Collaborated with cross-functional teams, including development, marketing, and customer service, to conduct comprehensive analyses of the loan app’s usage data, leading to enhanced user experience and product optimization. Utilized Thoughtspot Analytics to develop insightful dashboards that track user engagement, loan disbursement rates, and financial performance metrics, aiding in strategic decision-making for the loan app. Spearheaded automation initiatives in data processing for the loan app, significantly reducing manual effort in data validation and improving efficiency in credit risk assessment. Generated and quality-assured reports on loan app performance using Thoughtspot Analytics and Tableau, coupled with SQL to ensure data accuracy and consistency. Led the transition of customer loan data from an SQL Server to an Oracle database, utilizing the SDLC process. Designed and executed SQL queries for in-depth analysis of large-scale data, including user demographics, loan application patterns, and repayment histories. Conducted training sessions for various departments, focusing on leveraging insights from Tableau and Thoughtspot Analytics to understand customer behavior and improve user interactions with the loan app. Performed ETL operations to integrate diverse data sources into a unified view, creating comprehensive dashboards and reports in Tableau to support key decision-making in loan approval processes. Implemented an automation script for data cleansing in the loan app, correcting data inconsistencies and reducing manual data correction efforts by over 60%.