Yue Dai

Studied leading SEO keyword searching websites and developed fee models to determine suitable features for company websites.
Analyzed CEO-company search trends using Google Trend and identified correlations, market impact, and competitor effects.
Researched current refinance trends and key players and analyzed customer preferences through questionnaire insights.
Developed ML-driven model for optimal loan refinancing timing, integrated the model with a break-even analysis considering costs and profiles, and conducted thorough research on real estate finance to enhance the accuracy and effectiveness of the model.
Constructed and tested data pipeline and fully automated and interactive Tableau dashboards for internal stakeholders, enabling future research on product optimization.

Conducted statistical analysis, including Pearson's chi-squared test, and built predictive models, including Multinomial Logistic Regression, in Python to identify associations on financial data.
Conducted exploratory data analysis to understand patterns and trends in product popularity and the correlation between web traffic and orders over time using Python data visualization tools such as Matplotlib and Seaborn.
Led three interns and managed the team to conduct strategic and competitive analysis of Wisdom Capital and its competitors, including analyzing value chain, product interface, profitability, target customer groups, and overall industry trends and regulatory policies.

Gathered and cleaned user engagement data in SQL, conducted EDA to define project scope, performed statistical analysis, created data visualizations, built and tested XGBoost and K-means model in Python to provide prediction of customer behavior, with over 90% AUC-ROC.
Designed and implemented ETL solutions with over 200GB of data.
Maintained data infrastructure including building over 40 data tables, reconstructed the databased structure, improving the query efficiency about 50%.
Conducted and analyzed 3 A/B testing experiments to evaluate operational efficiency improvement.

Performed SQL queries to build data collection, storage, and processing infrastructure.
Analyzed business problems and converted report data to actionable items; delivered simplified and visualized analytical outputs.
Collaborated closely with cross-functional product and engineering team to extract meaningful business insights and create targeted marketing strategies based on their risk-return preferences.
Established a data exchange ecosystem for customers and defined roles and responsibilities for each stakeholder group.
Created detailed workflows for 10 end-to-end business processes.
Improved operational efficiency by 5%.

Studied leading SEO keyword searching websites and developed fee models to determine suitable features for company websites
Analyzed CEO-company search trends using Google Trend and identified correlations, market impact and competitor effects
Researched current refinance trends and key players and analyzed customer preferences through questionnaire insights
Developed ML-driven model for optimal loan refinancing timing, integrated the model with a break-even analysis considering costs and profiles and conducted thorough research on real estate finance to enhance the accuracy and effectiveness of the model
Constructed and tested data pipeline and fully automated & interactive Tableau dashboards for internal stakeholders, enabling future research on product optimization

Conducted statistical analysis (e.g., Pearson's chi-squared test), built predictive models (e.g., Multinomial Logistic Regression) in python to identify associations on financial data
Conducted exploratory data analysis to understand patterns and trends in product popularity and the correlation between web traffic and orders over time using Python data visualization tools such as Matplotlib and Seaborn
Led three interns and managed the team to conduct strategic and competitive analysis of Wisdom Capital and its competitors, which including analyzing value chain, product interface, profitability, target customer groups and overall industry trends & regulatory policies

Gathered and cleaned user engagement data in SQL, conducted EDA to define project scope, performed statistical analysis, created data visualizations, built and tested XGBoost & K-means model in Python to provide prediction of customer behavior, with over 90 % AUC-ROC
Designed and implemented ETL solutions with over 200GB of data. Maintain data infrastructure including building over 40 data tables, reconstructed the databased structure, improving the query efficiency about 50%
Conducted and analyzed 3 A/B testing experiments to evaluate operational efficiency improvement

Performed SQL queries to build data collection, storage, and processing infrastructure. Analyzed business problems and converted report data to actionable items; delivered simplified and visualized analytical outputs
Collaborated closely with cross-functional product and engineering team to extract meaningful business insights and create targeted marketing strategies based on their risk-return preferences
Established a data exchange ecosystem for customers and defined roles and responsibilities for each stakeholder group. Created detailed workflows for 10 end-to-end business processes. Improved operational efficiency by 5%