My specialties have a lot to do with data. I enjoy cleaning and transforming data to analyze and visualize. I’m always looking to learn new things and enhance my skills.
Experience in data ETL, data cleaning, data validation and process automation, mostly with dbt, Alteryx and Python.
Experience in designing dashboards and presentation decks using data visualization tools like Lookwer, Datorama and Power BI. Various experience from Excel charts to challenging CSS customizations.
Various experience in predictive analysis modeling like regression analysis on customer data and online media data to provide data driven insights. Most often using SPSS or R.
Experience in providing consulting services and building marketing and media strategies based on data analysis and research.
Sep. 2021 - Current
Manager: Jun. 2021 - Sep. 2021
Sr Analyst: Mar. 2020 - Jun. 2021
Sr Analyst: Nov. 2017 - Mar. 2020
Analyst: Aug. 2016 - Oct. 2017
May 2016
Courses: Database Management, Predictive Analysis, Marketing Research and Engineering, Spreadsheet Modeling, Business Analytics
Projects: Ran predictive analyses with regression models to provide data-driven strategic insights for healthcare clients
Launched and led a Japanese student organization, and organized several events.
Mar. 2014
Graduate thesis: “A Study on the Language of Advertising”
Aug. 2011 - May 2012
Courses: Economics, Marketing, Finance and Linguistics
Here are some of the interesting projects I’ve worked on in the past. Most of them involve data analytics, marketing research, and consultancy.
This client was a membership based organization for healthcare professionals, and was looking to improve their strategy to increase customer retention rate and revenue. We ran several analyses on their customer data to identify groups of customers that are more likely to return frequently and generate revenue, and if events and incentives are working and not working. Specifically, Market Basket analysis and RFM (Recency, Frequency, Monetary) analysis generated interesting insights. These 2 analyses helped segment customers based on their purchasing behavior, and we identified a customer segment that had the highest potential to drive an increase in ROI. Based on the findings, we’ve provided strategic recommendations on targeting and retaining this group of customers.
After studying data analytics for marketing at the graduate school, I had a strong desire to apply the skills to help other people. I reached out to a data scientist at the Lurie Children’s Hospital, and worked together to analyze patient admission records to look into opportunities to improve healthcare operational efficiency.
Our goal was to help identify patients who are likely to result in high use of care (e.g. readmissions), so that healthcare professionals can provide individual and customized care to those patients in advance to treat them efficiently. By running regression analysis on existing patient record data, we were able to identify factors that are likely to lead to the high use of care.
This client provides integrated technology solutions to healthcare providers to help utilize big data they produce every day in order to improve their operational efficiency. We conducted an online survey and interviews with healthcare professionals to understand customer needs and challenges, and built actionable strategic recommendations to target optimal customer segments. Based on in-depth analysis of the company, industry, and customers, our recommendations included strategies to penetrate the current market and develop potential markets to achieve continuous business growth.
Every year, thousands of new wines are produced, reviewed by professionals in the industry and given scores and tasting notes. Price of wine ranges from as low as $5 to as high as $5,000 (or even higher). Grape growing and wine making processes are complicated, and quality and brand reputation affect the price of wine significantly. But do $30 wine and $60 wine taste so different? How about $80 wine and $150 wine?
Using over 250,000 rows of wine review data, I am working to understand if different kinds of words are used to describe more expensive wine, and if I can predict the price of wine based on wine critics’ tasting notes.