Hello, I'm Jasmine! Welcome to my website where you can learn about my academic interests, and past projects! Currently, I work as a Data Scientist II at Guardian Life where I work with the Digital Marketing team and Underwriting team. I am currently profiling leads, improving an internal risk classification model, and developing a pipeline of bias tests for our underwriting model. Here is a rundown of my passion, and past work experiences:

My Passion

I am consumer obsessed. My passion in data science lies within the ability to understand people and their interactions to products, such as website features or in e-commerce applications. Here are my four favorite experiences so far that developed this passion of mine.

  1. At Guardian as a data scientist, I often worked collaboratively across teams, one of those included the underwriting team. To improve their process operationally and financially, I developed and deployed a triage model that will assess the underwriting value of ordering additional medical exams for an applicant by analyzing their lifestyle and medical health. In this project, I got to work on many complex data sources such as drug prescriptions, disclosed personal and family medical history, and lab scans to predict mortality risk. This experience has enabled me to finetune my technical expertise in modeling and also connecting to the key business objectives.
  2. I was the product owner and solutions architect to a Natural Language Processing toolkit I built for Nestle USA that had topic modelling and sentiment analysis capabilities. The ability to classify and label textual data such as product reviews and social media posts allowed me to drive business decisions with the focus of developing consumer alignment strategies. For example, I advised brand managers to keep or improve on certain characteristics of the product that consumers were actively praising and criticizing online. Another use case was being able to advise the marketing team on what kind of content users engage the most with on a social media platform.
  3. I worked with the Home Depot team to improve the traffic and conversion rate on their online home depot site. By analyzing site activities and clickstream data, I was able to develop a recommendation engine to suggest landing page destinations so that the average probability of the customer's conversion rate increases by 22%.
  4. I built a recommendation model during my data science internship at Aretove Technologies, predicting the next products customers would buy. I conducted customer journey mapping to analyze customer buying patterns. I also clustered customers with several defining features to recommend the likeliest product that would be ordered per customer segment.

I am hoping my future experiences will align with my academic interest in utilizing my software skillsets in R, Python, SQL, Web-programming languages, and my knowledge of operations research methods and statistics in the context of consumer experience and e-commerce business applications. Feel free to contact me via email at jcn66@cornell.edu or visit https://github.com/jasminecng9999 to view my work in more detail!

Resume