"As a soon to be  graduate from  UCSB, with a degree in Stats and Data Science, I am eager to further my expertise through a Master's in Data Science or Computer Science.”

Education

Bachelor of Science in Financial Economics

Columbia University, School of Professional Studies
Sept 2019-March 2021

Bachelor of Arts in Economics and Accounting

University of California, Santa Barbara
June 2022-Present
Technical Strengths
  • Excel
  • R
  • Python
  • SQL
  • JavaScript
  • Java
  • Typescript
  • Angular
  • Node.js
  • MongoDB
  • Unix
  • MongoDB
Experiences

Keo Technology Group – CEO & Data Head

Ed-tech startup building no-code, interactive, digital infrastructure for the education sector.
Shanghai, CN
-
June 2022-Present
  • Raised $590,000 USD Seed investment from US-based venture capital firm, AMINO Capital, and HK-based private equity firm, Mandra Capital
  • Spearheaded product management, design, and growth from zero to one achieving 5.5x MoM user growth (Jan-May 2022), 2.5MM+ total users, 50k daily active users, and 30% user weekly retention
  • Managed a cross-cultural 20-person team across software development, product design, and marketing and operation functions
  • Led digital media strategy, constructing dashboards and key review metrics to quantitatively assess marketing results, achieving 100+ MM cross-platform engagement, ≈$0.02 CAC, 250k+ social media followers
  • Implemented efficient management frameworks and software (OKR, Notion, Slack, Jira) to structurally reduce miscommunication and improve execution for onshore and offshore employees
  • Responsible for financial forecasts and budgeting from daily accounting to financial models and reports for investors.
  • Constructed an entire B2B SaaS pricing and sales funnel and signed 1.2 million users with notable clients spanning from public universities to private institutions, to Bytedance (parent-company of Tiktok)
  • Demonstrating team’s work to different audiences using multiple visualiatoin tools

EY-Parthenon –  Data Analyst Intern

Remote
-
12/2022~01/2023
  • Conducted market research on Chinese e-commerce and short video industries, gathered and analyzed data from various sources.
  • Synthesized research findings into a 40 page comprehensive report on the current state of the market and future trends.
  • Worked with 2500 lines of client excel data to summarize and analyze user data through using SQL and R
  • Conducted hypothesis testing to identify any real significance of different payment and product cart pages for mobile app to improve conversion rates.

Harvard Business School – Research Assistant

Research Project on Starbucks delivery service in China
Shanghai, CN
-
06/2019~09/2019
  • Reserached food delivery service market in China, specifically compared and contrasted closely between Meituan and Elema market position and strategy
  • Closely studied Luckin Coffee delivery service and formed Starbucks’ delivery service proposal by working exclusively with Eleme

China Universal Asset Management- Data Analyst Intern

Research Project on Starbucks delivery service in China
Shanghai, CN
-
06/2018~08/2018
  • Gathered, visualized, and cross-analyzed fund performance statistics with that of competitiors’ for product pitchdeck thereby boosting monthly sales by 22.1 percent
  • Worked with front-end sales representative for customer-specific data visualisation
  • Update daily fund performance and share with active customers
Works

Keo Space

Cloud Workspace for productivity-hacking and relationship-building.
Helping millions of individuals improve productivity, fight loneliness, and escape unsuitable working environments.
2.5m+
Trusted
total users
60m+
Platform
focused minutes
100m+
Cross-media
impressions
~3x
User
growth MoM

Keo Plus AI LMS

AI powered content creation
Building courses has never been easier. Work with your AI assistant to source, generate, and structure your course in minutes. Supercharging millions of creators and educators to curate the knowledge of the world.
The aim of this project is to build machine learning models to predict the forest cover type based on cartographic variables.

Machine Learning Project

Predicting Tree Cover Type
Employing advanced machine learning techniques, this project adeptly predicts forest cover types using cartographic variables, leveraging a rich Kaggle dataset to address key ecological and conservation questions with a blend of KNN, Multinomial Regression, Random Forest, and Boosted Trees models
Download report
Social Media Portfolio