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The University of Texas at Dallas May 2020
MSc, Business Analytics
Dean’s Excellence Scholarship’18
Uttar Pradesh Technical University, India May 2015 B.Tech, Electronics and Telecommunications Engineering TECHNICAL SKILLS
Analytic Tools: Python • R • SAP Business Objects • SAS • Advanced Microsoft Excel • VBA Database & Viz: Microsoft Azure • Microsoft SQL Server • PowerBI • Tableau • Salesforce Data Science & Analytics: Regression• Classification• Forecasting • A/B Testing • Machine Learning• Business Intelligence • Data Mining • Data Analysis • Data Warehouse • ETL • Decision Tree • NLTP/Text Analytics • Descriptive Analytics Certifications: Advanced Database and SQL Querying • Python for Data Science & Machine Learning • Tableau Certified BUSINESS EXPERIENCE
Carl ZEISS Meditec, Inc. – Dublin, California May 2019 – Present Business Intelligence Analyst
Data Visualization: Designed a revenue forecast model using PowerBI DAX to capture the product demand across North America, provided insights on contract sales and forecasted expected demand using statistics and calculated measures with 85% accuracy.
Database Administration: Developed the underlying algorithms and pipeline to fetch data from sources like Salesforce, SAP and Excel into SQL Server Database. Wrote SQL scripts for table creations, joins, relationships between SQL tables and subqueries to fetch the data required for automated reporting.
Business Intelligence: Executed data mining and detailed data analysis to present insights on limited sales of a device. Reported a potential improvement in the process of contract renewals for the product to improve the related revenue by 10% in US.
Predictive Analytics: Predictive Analytics to predict Churn Probability and to generate Churn Scores for customers, reducing the Churn rate from 20% to 16%. Used Feature importance to highlight parameters for churn rate (Regression & Classification)
Machine Learning: Developed a text analytics model with 82% accuracy using Python package NLTK and Multinomial Naïve Bayes Classifier for multiclass classification to identify the root cause of product related issue using internal notes and several product associated attributes to build the model.
1MG Technologies Pvt. Ltd - Gurgaon, India Jan 2018 – June 2018 Sr. Data Analyst
Forecast Model: Designed and piloted a prediction model using regression for efficient inventory forecast based on trends and historical data of sales and demand of the products in Delhi, showcased analytical skills and increased fulfilment ratio by 15%.
Tableau & SQL: Developed Tableau reports to analyze orders from difference regions, segmented the customers based on order history, showcased trend to forecast future demands, improved the service and reduced logistic costs by 10%.
PharmEasy - Delhi, India August 2015 – May 2017
KPI Reports: Researched the internal business process, used performance metrics/KPIs and market data to present business and expansion opportunities as pipelines to be considered by sales team, increasing the user base by 10%.
Teamwork: Mentored a team of associates in developing Power BI reports using data from several data sources showcasing leadership, ownership, communication skills and teamwork values. ACADEMIC PROJECTS (Github)
Telecom Churn Rate Prediction and Customer Behavior Analysis (SAS)
Developed a Logistic Regression model using SAS to predict which customers are likely to leave (churn) from the platform.
Exploratory data analysis and Maximum Likelihood Estimation to identify top parameters that affect churn in the model.
Used AIC and BIC to capture model efficiency and improvement after removing insignificant variables and recorded a hit ratio of 80%. Audit Risk Firm Classification using Supervised Machine Learning in Python
Used Linear Regression and KNN Regressor to determine the Risk Audit Score for 777 target firms.
Trained classification model (Decision Tree, KNN, Logistic Regression) to predict a firm’s Audit Class (Risk or Not-Risk).
Analysis on User Engagement Drop using SQL
Used customer database on SQL Server to analyze trend of customer activity on different devices, days of the week and age groups. Descriptive Analysis on drop in user engagement. Applied A/B testing to analyze the performance of a new feature introduced on App.