Experience

Intact

• Canada
• Insurance
• 700 & Above Employee

• Senior Data Scientist

  • Mar 2019 - Present

  This project is to predict high priority tickets and ticket volume from their IT Services Management. To predict priority 1 & 2 tickets, so that they can take preventive measures or fix the problem before it surfaces. Forecast the incident volume in different fields, quarterly and annual. So that they can be better prepared with resources and technology planning. This project is to predict high priority tickets and ticket volume from their IT Services Management. To predict priority 1 & 2 tickets, so that they can take preventive measures or fix the problem before it surfaces. Forecast the incident volume in different fields, quarterly and annual. So that they can be better prepared with resources and technology planning.



Birch Communications

• United States
• IT Services and IT Consulting
• 100 - 200 Employee

• Senior Data Scientist

  • Dec 2017 - Mar 2019

  Lead quality was based on manual categorization and highly depended on sales staff. As effective sales are dependent on lead quality, the objective was to explore Machine Learning to pre-categorize the lead quality and as result, expecting significant increase in sales effectiveness. The key goals are 1. Data exploration insights – Sales effectiveness. 2. ML model to predict the Lead Category. Lead quality was based on manual categorization and highly depended on sales staff. As effective sales are dependent on lead quality, the objective was to explore Machine Learning to pre-categorize the lead quality and as result, expecting significant increase in sales effectiveness. The key goals are 1. Data exploration insights – Sales effectiveness. 2. ML model to predict the Lead Category.



TD

• Canada
• Banking
• 700 & Above Employee

• Senior Data Scientist

  • Sep 2016 - Nov 2017

  The objective of the project was to predict the customer churn in their Credit card customer base. Understanding the variables that are influencing the customers to migrate so the company can redirect their marketing campaigns towards these customers to reengage these customers with the products. The goal was to study customer behavior and identify behavioral patterns that lead to disengagement with the product. The objective of the project was to predict the customer churn in their Credit card customer base. Understanding the variables that are influencing the customers to migrate so the company can redirect their marketing campaigns towards these customers to reengage these customers with the products. The goal was to study customer behavior and identify behavioral patterns that lead to disengagement with the product.



Citi

• United States
• Financial Services
• 700 & Above Employee

• Data Scientist

  • Jun 2014 - Aug 2016

  The main goal of this project was to develop predictive analytics to perform customer segmentation using various machine learning algorithms and statistical modeling for better understanding of customer behaviors and leverage the insights for better product offering, and efficient marketing. The main goal of this project was to develop predictive analytics to perform customer segmentation using various machine learning algorithms and statistical modeling for better understanding of customer behaviors and leverage the insights for better product offering, and efficient marketing.