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Zifan Dong

Research Assistant at Illinois Risk Lab

Based in Champaign, United States

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Seniority

Staff

Department

Research & Development

Location

Champaign

Industry

Insurance

Company size

27

Contact information

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Email

1 credit

z•••••••@asrm.illinois.edu

Phone

5 credits

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Background

About Zifan Dong

EDUCATION University of Illinois at Urbana-Champaign Bachelor of Arts in Mathematics, Double Major in Economics GPA: 3.85 Relevant Courses: Intro Computing: Engr & Sci, Statistics and Probability, Methods of Applied Statistics INTERNSHIP AND ACADEMIC EXPERIENCE KPMG Consulting Shanghai, China Data Analysis Intern May. 2021 – Jul. 2021 • Collected, cleaned, transformed, and normalized 10K+ of data focused on freight business industries with Python and SQL and performed feature selection through exploratory data analysis • Built Tableau dashboard to visualize analytical results, outlined the insightful findings, and provided recommendations and data-driven solutions to solve challenges • Collaborate with data scientists, engineers, and project managers to collect information and generate plans and effectively communicated technical results to business partners • Analyzed actions of brands for automation and intelligent systems in the freight business, processed data in pet polyester industry to identify the significance of data platform Deloitte Shanghai, China Research Analyst Intern Dec. 2020 – Jan. 2021 • Conducted in-depth research on small businesses across various industries, including cosmetics, clothing, electronic technology, education, and identified competitors, pain points, and advantages • Analyzed and interpreted data from new products of Erno Laszlo using SQL, presented search strategies to clients, made formal introductions of client companies to candidates, and helped a C-level candidate make decisions • Identified insights behind data and created visualizations in Python to present business strategies • Enhanced data processing and integration of historical data files in 2018 and 2019 and helped improve financial analysis efficiency by 30%

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