• Base SAS, SAS Predictive Modeling and Tableau Desktop certified programmer with credible analytical skills
• 5 years of experience as Software Developer in IBM and 1+ year in Data Analytics
• Proficient in SQL, Tableau, SAS, R and Python and its usage for exploratory data analysis, modeling, prediction, and forecasting
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Experience
Data Analyst Intern, IRIM – Oklahoma State University
-Implemented a logistic regression model with 68% accuracy to predict Fall 2019 retention using academic and student survey data
-Found the influential factors behind churn and reported the top 10% courses which were at the risk of low retention rate
-Worked on text mining and topic modeling on student survey data to segregate the student cluster that is likely to drop from OSU and reported the top five reasons behind the drop
-Created interactive dashboards using SAS Visual Analytics featuring student profile and course details in order to highlight the past 5 year trends in GPA drop
Senior Software Engineer, IBM
Project: Customer Management System (Airtel Africa) | Base Moridiale des Risqué de Credit (BNP Paribas)
-Worked on architectural design enhancement by performing trend analysis to understand usage patterns for 17 operating countries within Africa and made activity dashboards for each web-based application.
-Worked at the client headquarter in Paris to improve the SQL based aggregation logic used to compute credit rating and risk by making automated scripts, increasing the efficiency of calculation by approximately 10%
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Projects
Advertising Effectiveness | SAS Global Forum 2020
-Presented an E-Poster depicting the impact of Super Bowl advertisements on the stock prices of the products/companies
-Created a classification model with k fold validation achieving an accuracy of 65% to uncover the factors affecting the prices
Factors influencing Lung Cancer Mortality Rate | SAS Student Symposium
-Constructed a cross-sectional dataset by pulling in the last 20 years of data from various sources for 50 US states
-Created a panel regression model with 14% MAPE to understand the influential factors impacting mortality rate
-Performed regional segmentation to provide recommendations for each US region based on the corresponding significant factors
LTOT Prediction | Humana-Mays Healthcare Analytics Case Competition
-Explored the opioid perception data and used it to create a target variable which tells the LTOT behavior of every patient
-Created a random forest model which predicted LTOT behavior with 77% accuracy and shared preventive recommendations
-Recognized as one of the top 50 teams out of 473 teams which took part in this nationwide competition
Loss Recovery Optimization | Leading Auto Loan Financing group
-Analyzed the existing loss recovery model in order to predict an efficient segmentation and regaining strategy
-Created a logistic model and recommended viable clustering approach using k-means segmentation to optimize the in-house/outsourcing decision strategy for non-collateral accounts
Employee Retention | Business Analytics Case Competition
-Analyzed the financial impact of employee churn and created a retention model using logistic regression and k-means clustering
-Won the 2nd prize at this annual case competition for the analysis and unique recommendations
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