Gaganpreet Kaur Baansal

adkfqm@r.postjobfree.com +1-217-***-**** Champaign, IL,61820

Education

University of Illinois Urbana Champaign Aug 2019 – May 2021 Master of Science in Information Science GPA: 3.96 Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV), Bhopal, India July 2013 – June 2017 Bachelor of Engineering: Computer Science & Engineering GPA: 7.90/10 (Hons.) Technical Skills

AWS, Eclipse, Python, R, Hadoop, MySQL, Hive, RStudio, Tableau, Power BI, Jupyter Notebook, SQLite Studio, GitLab, MS Excel, MS PowerPoint, Weka, C, C++, Mainframe, .NET, SharePoint, Docker Professional Experience

Data Scientist Country Financial June 2020 – Present

• Reduced expenditure by $60,000 leading the optimization of business processes, working on various innovative projects leveraging Data Analytics and Data Science principles

• Facilitated requirements gathering sessions and documented current and future state processes by creating functional specification documents and process flow diagrams

• Improved claims experience for clients by reducing business cycle time and focusing on areas discovered using machine learning methods like Random Forest, Decision trees, SVM, Naïve Bayes and Logistic Regression

• Enhanced business operations using ETL pipelines built with Hadoop, R and Python, and leveraged interactive Tableau Dashboards to exhibit dynamic visualizations

• Identified insights and gaps in claims experience using root cause analysis of customer survey data

• Visualized results of statistical data models using RShiny Dashboard and generated reports

• Lead to cost reduction by implementing a predictive model for mileage prediction designing both classification and regression models using various python libraries such as Pandas, Numpy, Scipy, MatplotLib, Seaborn and scikit learn

• Curated Tableau Dashboard to show various key metrics for CIO, working with stakeholders to define meaningful metrics that are most indicative of performance

• Implemented a POC to identify better performing database between Microsoft Azure and SQL to make business processes more efficient and faster

Project Manager Business Intelligence Group, UIUC Aug 2020 – Oct 2020

• Strategized and carried out Knowledge Management for client leading a team of skilled consultants

• Analyzed ad fraud market to carry out vendor assessment based on different metrics

• Recommended ad fraud solutions based on identified risks to the client by carefully analyzing current and future trends Systems Engineer Infosys Dec 2017 – July 2019

• Recommended technology solutions to a major financial institution by accessing application maintenance issues using customer data to identify process gaps and positive trends

• Assisted a financial firm transition from mainframe to micro-services-based architecture resulting in performance improvement of 11% exceeding customer KPI

• Designed test cases for validations on micro services such as business validations, structural test cases and cardinality

• Reduced client investment 20% by implementing a new alternate issue tracking system replacing manual efforts

• Collaborated with stakeholders to document requirements and explore potential solutions Academic Projects

COVID19 Analysis Tableau Nov 2020 – Dec 2020

• Analyzed COVID 19 data to better understand overall effect of the disease throughout various states of the US

• Visualized data on various parameters such as total number of people affected, hospitalized and number of deaths Customer Segmentation Python Nov 2020 – Dec 2020

• Identified customer segment groups based on their spending habits by performing clustering using Python

• Executed data pre-processing with various NLP techniques & PCA followed by clustering using K means Company Site Selection R Nov 2019 – Dec 2019

• Processed, profiled, and analyzed dataset of areas of land to select potential sites for corporate headquarters for a company

• Performed trouble shooting to help in data cleaning activities to remove irrelevant records

• Performed descriptive and distributional analysis to explore relationships amongst data for site selection

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