Yiwen (Catherine) Xu

· Analyzed M&A deal data, industry benchmarks, and financial indicators using Python and Excel to support valuation models and transaction screening, improving research efficiency by 30% · Collected, cleaned, and validated large datasets from public filings, and industry sources, reducing data discrepancies by 20% and ensuring accuracy for investment memos · Built Excel models and summary dashboards to compare target company fundamentals, synergies, and market positioning, enabling the team to identify high potential acquisition targets · Collaborated with external clients to clarify data requirements, gather company disclosures, and verify operational details, strengthening communication accuracy and accelerating due diligence progress by 15%

· Conducted market research on 50+ Lidar companies, analyzing growth drivers, competitive positioning, and commercialization readiness to support sector level investment recommendations · Loaded and organized industry and financial datasets into a MySQL database, improving data accessibility and reducing manual retrieval time by 35% · Evaluated market trends and company performance using linear regression and ARIMA forecasting, identifying growth rate and potential investment opportunities · Defined key performance metrics and created Tableau dashboards to visualize market size, technology adoption, and company benchmarks, enhancing decision making efficiency for senior analysts

· Analyzed fund reconciliation records to identify discrepancies and data integrity issues, improving financial reporting accuracy by 18% · Automated reconciliation and routine accounting checks with Python, reducing manual processing time by 45% and minimizing human error · Produced D/W/M Excel reports covering cost, budget variance, and revenue segmentation, enabling finance managers to track performance more efficiently · Designed a self-serve Power BI reporting platform that centralized financial metrics, allowing cross functional teams to access insights and reducing ad-hoc reporting requests by 30%

· Conducted customer surveys and focus groups to analyze investment needs, risk tolerance, and product preferences, generating insights that informed new wealth-management product design · Analyzed customer demographics, service feedback, and market trends using SQL to identify product gaps and benchmark offerings against competing financial institutions · Built customer segmentation models using RFM to classify user groups by activity, value, and engagement level, enabling more accurate wealth product recommendations · Collaborated with product and branch teams to refine customer personas and align product design with user behavior, improving targeting accuracy by 20%

Relevant Coursework: Econometrics, Advanced Statistical Analysis, Hypothesis Testing & A/B Test Evaluation, Regression Modeling, Data Visualization, Microeconomics.