Data Analyst Machine Learning

RUNYI (PEGGY) PAN

*****@*******.*** cell: +1-607-***-**** LinkedIn

EDUCATION

Cornell University, The Charles H. Dyson School, Ithaca, NY, U.S. Aug 2023 – Expected Dec 2025 Master of Science in Applied Economics and Management, GPA: 3.5 Selected Coursework: Climate Finance • Machine Learning • Valuation Principles • Financial Statement Analysis Beijing Normal University (BNU), Business School, Beijing, China Sep 2019 – Jun 2023 Bachelor of Economics in International Economics and Trade, Minor in Portuguese, GPA: 3.7 Selected Coursework: Statistics • Econometrics • Finance • Corporate Finance PROFESSIONAL EXPERIENCE

Securities Services Intern, Shenwan Hongyuan Securities, Chongqing, China Aug 2022 – Oct 2022

• Performed in-depth statistical analysis of the business department’s operational performance for the current year and past five years; mapped in Excel the revenue structure to determine and visualize trends to be presented at the company’s semi-annual internal operations review meeting.

• Managed prospective client relationships by promptly addressing daily inquiries about financial services and products and organizing communication records; increased conversion rate of prospective customers, resulting in 20+ new clients.

• Tasked with overseeing 5+ private funds in support of new business initiatives; categorized and organized clients’ information based on risk preferences and investment capital to recommend financial products aligned with client needs. Data Analyst Intern, DiDi Global (Beijing Xiaoju Technology Co, Ltd.), Beijing, China Mar 2022 – Jul 2022

• Worked on 10+ sentiment analysis and business intelligence projects to support strategic adjustments, including designing surge pricing, optimizing driver subsidies, and evaluating driver satisfaction.

• Interviewed B-side drivers to gather feedback on application functionality, emcompassing safety perception, promotion campaigns and application usability.

• Utilized Python to collect fuel prices for 12 cities in Brazil and visualized moving trends to determine fuel price subsidies.

• Extracted targeted driver data using Hive SQL for different research purposes, designed and collected 500 in-app questionnaire results to assess drivers’ perceptions, and proposed product-design recommendations, such as strategies to engage non-loyal drivers.

RESEARCH AND PROJECT EXPERIENCE

Consulting Project, Renewable Energy Company Strategic Solutions Report Digital Business Strategy, SC Johnson College of Business Sep 2023 – Dec 2023

• Led a consultancy initiative for enSights, a Software-as-a-Service (SaaS) platform specializing in renewable energy operations, to support their strategic entry into the U.S. energy marketplace.

• Developed and delivered key strategic advisements of target customers, target regions and feasible business models that significantly influenced enSights' approach to market penetration. This also included a thorough dissection of revenue generation strategies, analysis of industry dynamics, and positioning within the competitive landscape. ThyssenKrupp Financial Valuation Report

Corporate Finance, Undergraduate Exchange Program at University of Porto Dec 2022 – Jan 2023

• Conducted a profitability analysis of ThyssenKrupp, employing various financial indicators in Excel to evaluate the company's operational and liquidity status.

• Applied Discounted Cash Flow (DCF) Model and Relative Valuation to analyze ThyssenKrupp’s share price, concluding that the company was undervalued.

Research Assistant, Beijing Normal University, Department of Economics Apr 2021 – Apr 2022

• Developed an innovative multi-factor investment strategy integrating supply chain and compared it with the classic multi- factor models to assess the strategy’s effectiveness within the New Energy Vehicle Industry.

• Constructed a portfolio of 748 stocks across 13 categories related to the supply chain of the New Energy Vehicle industry from the China Stock Market in Python.

• Achieved a significant increase in annual return from 47.74% to 98.21% and improved the Sharpe ratio from 1.95 to 2.77 by implementing the proposed strategy, using the iFinD platform and Python for excess return calculations. SPECIALIZED SKILLS

• Technical: Proficient in Microsoft Office (Excel, Word, Powerpoint), Stata, Python, R,SQL, PowerBI, Tableau

• Language: English; Portuguese (CAPLE B2)

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