Marketing, Data Analytics, Machine Learning

Dinesh-Kumaar Rajan

************.*****@*****.*** https://www.linkedin.com/in/rdineshkumaar 312-***-**** SUMMARY

• Passionate predictive modeler with professional work experience in the Data Analytics Industry

• Ability to manipulate complex data to solve real-world business problems and provide actionable insights

• Experienced in presenting recommendations to non-technical audience in a clear and concise manner

• Expert in creating customizable dashboards and proficient in MS Excel, SQL and Tableau EDUCATION

M.S. in Management Information Systems, University of Illinois at Chicago GPA: 3.74/4 May 2017 B.E. Electronics and Communication Engineering, College of Engineering Guindy GPA: 8.55/10 May 2014 SKILLS

Programming Languages : C, C++, Python

Statistical Tools : SQL, R, SAS, SPSS, Hadoop, Tableau, MS Excel, Hive, Google Analytics Databases : MySQL, MS SQL Server, NoSQL, MS Access PROFESSIONAL EXPERIENCE

Analyst, LatentView Analytics, India June 2014 – June 2015

• Extracted sales data by writing complex SQL queries and developed reports/dashboards on a regular and ad-hoc basis

• Automated regular reports using SAS Macros and optimized the report generation time

• Built a campaign analytics predictive model for identifying best sales lead to convert into opportunities

• Predicted customer churn using Logistic Regression which reduced product cancellation rate from 37% to 25% Graduate Research Assistant(Part-Time), University of Illinois at Chicago August 2016 – May 2017

• Extracted data from various datasets and aggregated them using pivot tables in MS Excel

• Created various reports based on the requirements of the manager and created intriguing visualizations AWARDS

Winner – Credit Risk Modeling - Data Analytics Challenge – Chicago IL March 2016

• Built a predictive model in 4 hours to find which customers to lend from a list of prospective loans

• Extracted features and evaluated different models which increased the model performance ACADEMIC PROJECTS August 2015 – May 2017

Customer Response Modeling for a Marketing Campaign

• Developed a response model using CRISP-DM framework to upsell a banking product(HELOC) to the best customers

• Presented the results in non-technical terms to marketing executives from the banking industry

• Evaluated using Gains chart which showed an increase of 20% in the number of responders for the top two deciles Market Segmentation for Retail Banking Campaign

• Segmented US population using household demographics data to aid an acquisition campaign for customers

• Created 5 clusters utilizing the factors obtained by factor analysis and evaluated the clusters with pseudo-F statistic

• Profiled the characteristics of each cluster and presented unique recommendations to target each cluster Cost optimization for a Direct Marketing Campaign

• Built a classification model to identify potential donors to improve cost efficiency of a direct marketing campaign

• Utilized several machine learning algorithms to find the best predictive model to maximize profit On-time Performance Analysis of Delta Airlines

• Performed causal analysis of flight delay from Bureau of Transportation Statistics data (over 6M records)

• Identified various patterns and trends to improve the airline’s performance in comparison to its competitor Web Traffic performance of a website

• Applied various features of Google Analytics to set up goals and funnels and tracked the performance of a website

• Leveraged visitor data over time and provided recommendations to improve website

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