William Han, Asa, Maaa Email & Phone Number

Phoenix, Az, Us

▪ Established the business model by defining detailed investment criteria, allowing better time allocation to potential deals.▪ Advised other general partner groups who were struggling with underwriting (property valuation) to strengthen their offers.▪ Revamped an existing commercial multifamily property valuation tool to be more dynamic, conservative, and robust.▪ Observed a lack of quality training tools for underwriting and started hosting a weekly acquisition workshop with more than 20 people to provide education on the modeling process, which improved community engagement and knowledge.

New York, Ny, Us

▪ Developed and certified actuarially sound capitation rates for state Accountable Care Organization (ACO) and Managed Care Organization (MCO) acute care programs, covering approximately 1.1 million lives and totaling over $7 billion.▪ Owned and launched a forecasting tool to trend 2.5 years of data, estimating the expense of providing health care services.▪ Led cross-sector teams of 5-10 employees on multiple projection and reconciliation models to account for policy changes.▪ Forged and cultivated key client relationships through consistent communications, financial reviews, and presentations.▪ Delivered a presentation on the rate setting process to over 100 representatives from various state health plans.

New York, Ny, Us

New York, Ny, Us

▪ Identified key crime trends in Phoenix, AZ by zip code and time of day using an interactive dashboard built with R Shiny app on over 350,000 criminal public records to provide resource allocation recommendations to the local police department.▪ Built machine learning models (Random Forest, Gradient Boosting methods including LightGBM and XGBoost) to predict house prices in Ames, Iowa. The models were used to identify key housing features important for maximizing home value.▪ Forecasted future liquor sales of Hy-Vee using time series analysis and clustering based on location and products so their resources could be allocated more efficiently. Relational databases were utilized to handle more than 20 million sales records.▪ Recommended best value wines by analyzing wine notes from over 45,000 wine data scraped using Python (Selenium).

New York, Ny, Us

▪ Educated the actuarial department about the usage and flexibility of Power BI▪ Prepared extensive interactive data visualization reports using Power BI to communicate actuarial estimates▪ Performed actual vs. expected analysis on loss development▪ Validated recovery ratio assumptions used in models ▪ Assessed ALAE ratio projections by comparing the industry’s benchmarks to the company’s actual data▪ Collaborated with other interns to define the scope of peer to peer insurance and created a business recommendation

Bachelor Of Science (B.S.), Applied And Computational Mathematics, Business Finance Minor

High School