• Prepared data science solutions to aid in decision making and to automate business processes, reducing load on policy examiners • Produced predictive models to optimize the title production process and provide title turn time predictions to mortgage lenders • Wrangled large, complex transactional data sets to provide insight into opportunities to improve or automate the business processes

Assist professors and instructors within the Statistics department in developing lesson plans while working on the final stages of my PhD dissertation. During the class sessions, I actively engage with students to improve the overall learning process while fostering a safe class environment. In addition, I also lead lab and study groups, the grading of class papers, and preparation of course lectures.Contribution Highlights (Department of Statistics): ★ Serve as a graduate instructor for undergraduate Statistics courses. ★ Prepare and grade classwork and manage the proctoring of all tests and quizzes. ★ Assist department faculty in assessing coursework and papers, as well as logging all student grades. ★ Manage and schedule weekly student office hours while organizing teaching materials consisting of lecture notes, answer keys, and supplementary files.

• Supported teaching faculties in department of statistics on a research project that aims to evaluate the overall performance of undergraduate students enrolled in statistical courses through data analysis and statistical modeling• Utilized R studio to perform exploratory data analysis, such as, data visualization and missing value imputation• Trained three statistical models (linear, logistic and ordinal regression) to detect the influential features on students’ course performance, and proposed suggestions that led to 26% increase in the course pass rate

• Collaborated with two team members working on a project which aims to make an accurate prediction for the processing time of property title clearance • Wrote MySQL queries to export the data of 400,000 rows and 76 columns from remote database and utilized Python to implement exploratory data analysis, outlier detection and removal, missing value imputation, feature selection, and engineering, numerical data scaling, categorical data encoding • Derived a new prediction model which is based on K-nearest neighbor regression, and compared with previously built models, our developed model can further reduce mean square error by almost 40% • Participated in the innovation week project competition organized by the company and worked with six team members on a project which aims to detect if the uploaded profile photos meet the related requirements (for example, no sunglasses) • Present results to team’s global head and wrote requested executive summary detailing value proposition and strategy to senior leadership. As a result, our project won the biggest business impact award