Apurwa Prasad 313-***-****

New Jersey, ***** add5os@r.postjobfree.com

www.linkedin.com/in/apurwaprasad

EDUCATION

Master of Science - Business Analytics and Project Management Jan 2019 - May 2020 University of Connecticut, School of Business, Hartford, CT GPA 3.8/4.0 Bachelor of Technology - Computer Science Aug 2011 - Jun 2015 Punjab Technical University, India GPA 3.8/4.0

TECHNICAL SKILLS

• Languages: Python (NumPy, Pandas, scikit-learn, SciPy, keras, re, nltk, statsmodel), MySQL, R (shiny, ggplot2, dpylr), Java, C++

• Tools and Technologies: Tableau, Jupyter Notebook, PostgreSQL, R Studio, SAS JMP Pro, SAS Enterprise Miner, Microsoft Visio, Excel, Macros, VBA, Google Analytics, Hadoop (Sqoop, Pig, Hive), Oracle Siebel CRM, REST and SoapUI.

• Statistical Analysis & Modeling: Linear and Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree and Random Forest Classification, K-Means and Hierarchical Clustering, Association Rule Learning, Neural Networks, Dimensionality Reduction, Feature Engineering, Model Selection & Boosting, Time Series Forecasting and Text Mining, Hypothesis Testing, Market Basket Analysis, and A/B Testing.

• Database: Oracle, HDFS

• Cloud Platforms: Amazon Web Services

WORK EXPERIENCE

IBM – Analytics Consultant Advanced Analytics Dec 2017 - Dec 2018

• Customer engagement for Fortune 500 company

Forecasting: Led a team of 3 analysts to predict future demand using random forest with an accuracy of 67% to manage product subscriptions. Worked closely with the key business stakeholders to understand customer relationship management (CRM) and extracted insights to increase the effectiveness of project roadmaps. Customer Churn: Analyzed customer subscription history and their behavior to predict churn by using logistic regression. Used strategies based on segmentation to achieve better customer targeting and recommended changes in the current business process, saving ~$500k by retaining customers.

Data Reporting and Visualization: Used SQL to ingest client data into the internal tool by designing process flows and to extract detailed and rolled up data. Created interactive dashboards to analyze customer churn and streamlined ad-hoc analysis to develop KPIs. Tracked KPIs to measure performance and the effectiveness of marketing on churn. Assisted stakeholders in the strategy- making process, for improving customer experience in poor-performing metrics.

(Tools: Python, SQL, Tableau, Oracle Siebel CRM, Microsoft Excel) Cubastion Consulting Pvt Ltd – Associate Consultant Business Intelligence CRM June 2015 - April 2017

• Supervised machine learning and marketing recommendation for $1B retail company Predictive Modeling: Performed exploratory data analysis and used gradient boosting model to predict future demands to optimize inventory and provide estimates for products, which led to an increase in sales by 12%. Web Analytics: Analyzed the impact of several iterations of seasonal promotions on the product website by using A/B testing to test which version gets the best conversion rate and increased the revenue by 3%.

• Business Intelligence for leading $11B telecommunication company Product Recommendation: Analyzed 120k survey records to make informed, intelligent decisions to improve customer experience through actionable insights. Implemented market basket analysis to uncover association rules to influence the customer to buy product combinations.

Data Visualization: Built dashboards using BI tools to monitor key performance indicators like the best-selling products and peak season sales to communicate future opportunities. Automated jobs using scripting to produce real-time data and audit reports, thereby reducing manual reporting effort.

Data Modeling: Modified company’s customer database by designing process flows, ER diagrams by identifying entities and their attributes. Analyzed customer CRM lifecycle, lifetime value and assisted in data modeling activities.

(Tools: Python, SQL, Tableau, OBIEE, Oracle BI Publisher, Oracle Siebel CRM, Microsoft Excel, SAS JMP Pro) ACADEMIC PROJECTS / DATA CHALLENGE Jan 2019 – Dec 2019

• Life Insurance Policy Churn: Analyzed 3M+ data to understand the pattern and underlying reasons for churn by using research and survey. Predicted customer churn using decision tree with an accuracy of 78%. Discovered common associations rules to create customer profiles using apriori based association. (Tools: Tools: SAS JMP Pro, MySQL, R, R Shiny, Python, Tableau)

• Potoo Data Challenge: Analyzed the stock price of the publicly listed companies on amazon with dataset. Deep dived stock prices and stock indicators to create dashboards to find insights and detect the impact of stocks on sales. (Tools: MS Excel, SAS Enterprise Miner, Python, Tableau)