Data Engineer

Nikita Dodda www.linkedin.com/in/nikita-dodda/

**** * ********** **, *** 2074, Tempe, AZ - 85281 206-***-**** ******.*****@*****.*** Summary

Graduate student with strong problem-solving abilities and over one year of experience in project management seeking a full-time position in the field of Data Analytics offering responsibility & opportunity for growth. Education

W. P. Carey School of Business at Arizona State University August 2019 – May 2020 Master of Science in Business Analytics (MSBA) Tempe, AZ Technical Skills

• Machine Learning • Enterprise Data Modeling • Statistical modelling • Quality Management

• Operations Research • Decision Modeling • Project Management • Marketing Analytics

• Languages: Python, R, MYSQL, C, AMPL mathematical programming

• Tools: Tableau, Minitab, Advanced MS Excel, Primavera P6, MS Project, Stat tools, @Risk, Precision Tree, MATLAB Professional Experience

Student Consultant January 2020 - present

Ping Inc Tempe, Arizona

• Analyzing the data of 1.2 million golf shots by utilizing Arccos Shot Data of 11,000 players using MySQL.

• Building a golf club recommendation system algorithm using python to provide unique insights for each golfer. Project Engineer June 2016 - Feb 2017

Maitri Interior Projects Pvt. Ltd. Bangalore, India

• Prepared Master Schedules, using Microsoft Office Project and Coordinated construction activities, resources, equipment required for an interior project under Jones Lang LaSalle Incorporated (JLL) for client Accenture PLC.

• Created MS Excel Pivot tables and Dashboards to visualize the data of weekly progress, Safe man hours achieved, and resources procured for the projects.

Academic Projects

• SQL - Designed a relational data model for a virtual footwear company that contained product, order, customer, shipment data. Implemented business logic & transformed the source data to generate analytical datasets of interest using star schema. Proposed business strategies to improve sales of the business by analyzing and gaining data insights using SQL queries.

• Classification using Ensembles - Used bagging and boosting techniques to predict auto insurance fraud on Kaggle dataset. Built random forest and gradient boosting ensemble models obtaining an accuracy of 96.17% and 91.36% respectively by performing hyperparameter tuning and grid search.

• Classification Models (Homesite Quote Conversion – Kaggle Dataset) – Compared accuracies among various supervised learning algorithms like Random Forest, MLP, SVM(Linear), KNN and one layer of stacking with an ensemble of Gradient Boosting, Random Forest & Decision Tree algorithms to predict if a customer will purchase homeowner’s insurance at the quoted price. Applied SMOTE technique as the data was highly imbalanced.

• Decision Modelling – Optimized employee head count required in each shift of a day for Zhao Rui, a company in China, using Solver add-in-MS Excel, to achieve demand forecasting for 3 months with minimum labor cost.

• Logistic Regression (Stat Tools) – Performed logistic regression on Pima Indians Diabetes (Kaggle Dataset) to predict if a patient is diabetic or not. Improved overall accuracy of the model from 78% to 89% by identifying statistically significant features using P-values and correlation among the input features.

• K-means Clustering– Created and optimized clusters for a given time series data after normalizing weekly sales info of over 800 products. Visualized the pattern of time series in each cluster using python’s matplotlib and seaborn libraries.

• Text Mining: Predicted customer churn on semi structured data sets of a telecom service company using NLTK library. Obtained 87% accuracy with wrapper method of feature selection and random forest classifier.

• DOE and ANOVA analysis (MINITAB) - Constructed a Lego car and performed 5 factors 2 level full factorial DOE analysis to maximize the distance travelled by the car from a pre constructed ramp. Professional Development

Student member – Software development association (SoDA) club at Arizona State University

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