Class of 2021

Photo of Junpeng Marshall Jiang

Junpeng Jiang graduated cum laude from University of California, Los Angeles (UCLA) in March 2020 with double majors in Applied Mathematics and Statistics. During his school years, Junpeng has been actively seeking opportunities to leverage his data science toolkit in real-world problems. He participated in both 2019 and 2020 ASA DataFest Competitions, earning a finalist award in the first year and the best visualization award in the following year. During the 2019 DataFest competition, he led a team in identifying potential indicators of players’ fatigue in Canadian Women’s Rugby team. With the help from his team, Junpeng implemented principal component regression and probabilistic k-means in R to cluster observations and generate new fatigue measurements. Based on these exploratory analyses, Junpeng and his team delivered individualized training plans for the Rugby team that expect to boost players’ performance up to 35%. In DataFest 2020, Junpeng completed a project that explored the impact of COVID-19 on consumer goods. After web-scraping historical data from Amazon and Google Trend, Junpeng and his team correlated the price trend of different kinds of goods with their google search frequencies. Junpeng condensed heatmaps, animated line charts and many other plots into a R Shiny dashboard, which allowed the audience to gain visibility into consumers’ buying behaviors since the pandemic started.

To also apply his data analytical skills in a business setting and acquire more hands-on working experience, Junpeng sought the chance to work as a financial analyst intern in China Minsheng Investment Group (CMIG) in Shanghai. Junpeng was responsible for analyzing target companies’ financial statuses and reporting trends and fluctuations in their financially significant indexes. To leverage large-scaled external data and address the constant need for data consolidation and visualization, Junpeng constructed Python templates to quickly generate intelligible timeline plots and tables of target companies’ financial indexes. These templates not only accelerated his work but also assisted the investment team in reproducing consistent and modifiable company reports.  

After completing a wide range of quantitative projects and internships, Junpeng built up his data-driven skill set and amassed his experiences of working with datasets from various sources and industries.  Fueled by his passion for learning and career aspirations in data science, Junpeng looks forward to enhancing his data analytical expertise and applying his skills in more practical problems through the MSiA program. Junpeng is confident that, upon finishing this program, he will become an asset of the cohort and a well-rounded professional who is ready to launch a career in data science.