Data Analyst Sales

Kunal Indore

Cincinnati, OH +1-513-***-**** aderjb@r.postjobfree.com www.linkedin.com/in/kunalindore http://rpubs.com/indorekm EDUCATION

University of Cincinnati, Cincinnati, OH Master of Science, Information Systems (GPA: 3.9) April 2020 University of Mumbai, Mumbai, India Bachelor of Engineering, Computer Engineering June 2015 TECHNICAL SKILLS

• Analytical Tools: R, Power BI, Tableau, SAS, Python libraries for analytics (NumPy, Seaborn, Pandas, Matplotlib), Excel

• Programming: Python, Java, C++, SQL, REST, Angular, JSON, JavaScript, D3.js, .Net, Perl

• Certifications: Data Analyst Graduate Certificate

• Additional Skills: Data Analysis, Statistical Skills, System design, Development, Requirements Gathering, Agile & Waterfall Methodology

PROFESSIONAL EXPERIENCE

The Kroger Company (Data Analyst) June 2019 – Present

• Designed and developed 3 interactive dashboards using R shiny and Power BI to analyze and identify trends in KPIs like BOH(Inventory) Accuracy and Pick up fill rate to improve in stock processes and customer experience

• Built a classification model to categorize expenses posted by Kroger associates based on its description using text mining with accuracy of 80% thereby increasing the efficiency and eliminating manual efforts of categorization

• Automated Barcode generation process using R functions for weekly scans of items, reducing manual hours by over 95%

• Built, validated, and automated robust and scalable reports using R markdown and Excel to monitor sales and in stock processes and its metrics to analyze its impact on Inventory and Sales activities

• Extracted, clean and transformed Sales, Inventory, Ordering, Pickup and Delivery data from Kroger databases IBM DB2 and DB20, MS SQL Server and Apache Hadoop using advance and optimized SQL queries

• Designed and developed Tableau Dashboards to visualize daily and weekly trends in inventory accuracy across Kroger stores

• Worked with store associates to build customized solutions according to their requirements and built BI reports with tidyverse and visualizations using ggplot2, that identifies actionable insights impacting Inventory Management, Ordering and Sales

• Built a model using python libraries that decides items to scan for MDC process based on different KPIs in Kroger data

• Built a daily report in MS Excel using Pivot table and VLOOKUP to analyze ‘high risk’ facilities by calculating Days of Supplies to ensure optimized allocation and timely supply of essential items to associates across more than 2700 stores and offices Capgemini India Pvt. Ltd. (Associate Consultant) June 2015 – July 2018

• Identified the technology stack and designed the most efficient solution for implementation of HPE Data Protector’s user interface module for report generation and processing

• Created the plan to extract critical data from the application based on which predictive and prescriptive analytics was applied for forecasting

• Conducted daily scrum meetings, communicated, and coordinated with the product owner to change and optimize the code as per client’s requirements

• Predicted the number or size of backup per user, per year and month with linear regression model

• Developed a proof of concept demonstrating the advantages of Angular 5 over AngularJS by creating a feature by feature

‘champion vs challenger’ comparison

• Developed UI of the web application that visualizes data backup based on descriptive, predictive, and prescriptive analysis allowing users to make informed decisions using JSON, Angular, Typescript, HTML5 and D3.js ACADEMIC PROJECTS

• Analysis on Titanic Dataset using Python January 2019

• Analyzed the titanic datasets using NumPy, Seaborn, Pandas and Matplotlib libraries and concluded that age, sex, and status of an individual determined the survival probability of the individual

• Analysis on Boston Housing Price Data using SAS March 2019

• Determined various socio-economic factors that affects the prices of a house in Boston to draw strategic insights by applying methods available in SAS for univariate, bivariate analysis, and hypothesis testing

• Logistic Regression with German Credit Scoring Data using R March 2019

• Analyzed credit scoring data and created a Logistic Regression Model to predict whether a loan applicant will default the loan or not using stepwise regression and LASSO variable selection methods

• Created a Classification tree model, performed In-sample, out of sample and cross validation methods to compare the models and select best performing model based on deviance, Area under curve and false negative rate

• Visualizations on HPIRegions Dataset using Tableau March 2019

• Created a story using multiple dashboards containing visualizations to compare sales volume by region and date

• Created dashboards using visualizations that identify number of employees in USA using maps

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