Data Engineering

Shresta Balerao 848-***-**** • GitHub • LinkedIn • *******.*******@*****.***

SUMMARY:

SAS Certified Statistical Business Analyst and Stanford Certified SQL Professional with over 2.5 years of expertise in unveiling data driven business insights and providing business recommendations by designing captivating dashboards, statistical analysis, data modeling and data mining techniques

EDUCATION:

Master of Business Analytics and Project Management, University of Connecticut, GPA: 3.68/4.0 April 2017

Bachelor of Engineering in Civil Engineering, CBIT, Osmania University, India, GPA:3.7/4.0 June 2014

SKILLS:

Statistical Analysis and Modeling Techniques: Linear Regression, Logistic Regression, Decision Trees, Neural Networks, Time Series Forecasting, Text Mining, Natural Language Processing, Principal Component Analysis, Clustering, Hypothesis Testing, Market Basket Analysis, Customer Segmentation, Data Visualization, A/B Testing

Mathematical Optimization: Linear Programming (Network models, Integer Optimization) and Non-Linear Programming

Analytical Tools: R, SAS Base, SAS JMP, SAS Enterprise Guide, SAS Enterprise Miner, SQL (Oracle), Tableau, Excel

EXPERIENCE:

University of Connecticut, CT

Research Assistant - Data Analytics – New York City Transportation and Co2 Emissions August 2016 – Present

Performed Exploratory data analysis for NYC taxi, Citi bike and NYC subway turnstile data using R and Tableau to know demand for each transportation system. Examined demand of Citi-bikes at each subway station and recommended additional subway stations to set up Citi bike stations in order to capture demand

Examined green impact of Citi bikes in city of New York by analyzing the greenhouse gas emissions, Co2 in particular to identify significant contributors. Addressed issue of Co2 emissions caused due to NYC taxis by proposing a solution involving Citi bikes which reduces Co2 emissions by 3%

Data Science Consultant – Customer Segmentation and Marketing Strategy

Implemented Classification and regression trees, Logit and Random forest models in SAS JMP to predict whether the client will subscribe for the term deposit or not. Performed customer segmentation to target customers in order to maximize subscriptions

Infosys, Hyderabad, India

Data Analyst, Global Financial Services Attrition and Retention Project August 2014- December 2015

Effectively communicated with client to understand requirements of Ad-hoc reports and to provide an estimate of time to generate reports. Also, Generated Ad-hoc reports by extensive querying using SQL and developed corresponding visualizations for generated reports to examine number of new products or services purchased for banking client, it paved way for business to understand longevity of their services and develop new strategies to retain or launch products/services

Transformed stakeholder’s requirements into reporting deliverables using Tableau dashboards and SQL to provide business insights and strategic recommendations in marketing and supply chain areas which improved revenue by 2%

Conducted root cause analysis on existing customer database to unveil factors effecting service cancellations within organization which initiated business to take preventive measures to overcome customer attrition

Conducted survival analysis on customer data to identify their key behavioral attributes resulting in churn by developing survival models like Kaplan-Meir method in R which decreased churn rate by 1%

ACADEMIC PROJECTS: January 2016 - December 2016

Business Process Re-engineering for IELTS score reporting (SQL, Microsoft Visio):

Improved efficiency of IELTS score reporting process by 25% by re-engineering and introducing a transactional database. Designed normalized data model (ERD), and developed centralized database using Oracle SQL and Microsoft Visio

HR Employee Attrition (SAS JMP, Tableau):

Performed exploratory data analysis to analyze leading variables that are responsible for attrition of HR employees in a company and developed tableau dashboards to understand statistics of reasons for attrition

Forecasting USA Lead production (SAS Enterprise Miner):

Forecasted USA's Lead production based on historical data of total 30 years which follows decreasing linear trend and built Autoregressive Integrated Moving Average (ARIMA) model to forecast lead production for next 7 years

Predicting medical expenses in United States (SAS JMP, R):

Predicted annual medical expenditures by estimating overall risk of health care system and performed data modelling such as lasso and ridge regression to determine several factors that affect medical expenditures

Twitter Sentiment Analysis (R, Sentiment Analysis):

Implemented text analytics on Apple Inc tweets and built CART model (for Interpretability) to identify significant words that drive sentiment of tweet to be positive or negative. Also, built Random forest model (for Prediction Accuracy) in predicting sentiment

Google AdWords – Optimizing online Advertising (Linear Optimization, Excel- Analytical Solver Platform):

Implemented linear optimization model to identify optimal number of times google should display advertisements for different vendors so as to maximize revenue

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