Data Analyst

VIKAS SRIKANTH 469-***-**** *****.**********@*****.*** Linkedin Github Portfolio

Analytical professional with 2+ years of experience in generating and analyzing data reports for management. Technical expertise in collecting, analyzing, interpreting, and modeling large datasets by ensuring the quality and accuracy of data. Strategic mindset that focuses on problem solving tasks and maintaining priorities. Seeks to leverage experience in a Data Analyst role.

SKILLS

Modeling Techniques : Regression, Classification, Clustering, Predictive analytics modeling using SAS

Programming & Analytics : Programming in R, Python, Tableau, Microsoft Office Suite (Advanced MS Excel, MS Word, MS PowerPoint), statistical analysis, statistical modeling, Adobe Analytics, Site Catalyst, Dynamic Tag Manager

Databases : MS SQL, MySQL, Access (large and complex SQL databases)

PROFESSIONAL EXPERIENCE

Data Science Intern, KidKraft, Inc. May 2019 – Jan 2020

Suggested 4 new toy ideas to increase revenue by 15% annually by utilizing web scraping techniques to extract information and built trend forecasting models

Implemented Agile Planning & Execution (APE) Framework and RFM analysis to build product and customer segments to identify and improve opportunities in sales and pricing

Designed and automated 3 dashboards and reports to track KPIs for leadership on product sales, inventory management and profitability by exploring multiple data sources

eCommerce Data Analyst, DigitalShopper (Academic Industry Engagement) April 2019 - May 2019

Managed inventory of more than 1.5 million SKUs and performed POS and inventory data analysis to provide insights into category growth, highlighting top performers and increased the sales by 16%

Created 4 visually interactive dashboards on products and order data using Tableau for ‘Channel Advisor Connect Conference’ by leading a team of 5 members

Extracted, interpreted and analyzed orders data to identify key metrics like inventory to sales ratios, conversion rate, etc. Transformed raw data into meaningful, actionable information to accelerate data-driven business decisions.

Applied statistics to answer some of the business questions like best time to promote products, recommendations to customers, etc

Data Analyst, Lowe’s Companies (Retail) Dec 2016 - Jun 2018

Awarded as SPOT for an increase in eCommerce sales level by 32% in multiple venues and expanded through website modifications and improvements in functionality and navigability

Increased traffic rate and eCommerce profitability by 12% with a 3% increase in the customer base to elicit better business decisions by providing forward-thinking and web data analysis using multiple data sources

Established key performance indicators (KPIs), risk factors, and developed dashboards and reports that guided the higher management with updated sales performance

Led a team of 11 contractors during Online Delivery Scheduling project and demonstrated strong analytical skills like problem-solving, attention to detail, collaboration and communicating results to higher management

EDUCATION

The University of Texas at Dallas, Dallas, TX Aug 2018 - May 2020

Master of Science in Business Analytics

Coursework: Econometrics, Statistics, Applied Machine Learning, Web Analytics, Operation Research, Programming for Data Science

Visvesvaraya Technological University, Bangalore, India Aug 2012 - July 2016

Bachelor of Science in Electronics and Communication Engineering

ACADEMIC PROJECTS (Github)

Airbnb Host Analysis and Price Prediction Numpy, Pandas, Sklearn, NLTK, Matplotlib, Seaborn

Performed data cleaning, EDA, imputation and feature scaling to analyze data over 10K rental listings based on 96 variables.

Predicted the price of listings by fitting regression models such as Linear, Ridge, Lasso and ensemble methods and obtained median absolute error(MAE) of 0.2689

Google Analytics Customer Revenue Prediction R (Analyzing real-world data sets)

Constructed a predictive model in R with a Root Mean Squared Error (RMSE) of 1% for the customer revenue prediction

Forecasted the impact of factors such as device, traffic source, social engagement type, and time spent by a visitor in shopping

Data Visualization for Nike Manufacturing Map Tableau

Analyzed data and designed a dashboard to determine the relationship between factories, workers, and 4 different product types

Thoroughly investigated the data of 42 countries to highlight the top 5 contributing factors to Nike’s success

Predictive Analysis of Folgers Coffee Product Width SAS, R Studio

Performed statistical analysis to study how demographics and store-promotions affect brand choice

Implemented RFM Analysis to identify the most profitable / frequent buyers and provided recommendations on the type of customers to target

Performed timeseries analysis (ARIMA model) to forecast sales and predict the future sales by analyzing the past records.

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