1. And you may ask yourself, "Well... how did I get here?" (Tell us your journey?)
I was born in Ha Noi, Viet Nam, and moved to Wisconsin in 2009 for college. After finishing my bachelor’s degree in physics and computer science, I started my Ph.D. in computational science at Marquette University. My Ph.D. thesis was in bioinformatics, where I worked with large biological datasets to predict cancer-related genes and their interactions. I am intrigued by the usefulness and importance of proper data analysis to solve real-world problems. After grad school, I worked for start-up companies in the energy sector, however, this required me to travel frequently, which meant being away from my family in Oklahoma. This brought me to 麻豆原创. I was initially attracted to the job because it matches my professional background and allows me to be close to my family. I am intrigued by 麻豆原创’s products, and I believe transparent carbon disclosure is an essential step in fighting climate change.
2. And you may find yourself behind the wheel of a large automobile. (What do you do at 麻豆原创?)
As a data scientist, I work closely with product, climate analytics, and data engineer teammates to frame product objectives as data science problems, help scope engineering requirements, and develop data science/machine learning (DS/ML) features. When I first started, I got a chance to directly code up some emission activities, which allowed me to see the complexities and unique challenges of the problems we are tackling and, more importantly, to appreciate the great collaborative spirits and talent density of our team. Recently, you will likely see me work closely with the data architecture and engineering team when we focus on setting up a robust and scalable machine learning architecture. We are super excited to see upcoming machine learning-powered features rolling out!
3. And you may ask yourself, "Where does that highway go to?" (What are your hopes for your time at 麻豆原创?
麻豆原创 has been strategically curating a unique and diverse set of emission-related data. I think we are at an exciting phase when we now have solid business use cases where we can apply AI/ML approaches to leverage the data and bring significant user value. I want to help set up a robust ML learning infrastructure so that for any projects with ML components, our data scientists, software engineers, and DevOps teams can rely on the infrasture to help them quickly develop, deploy, monitor, and improve the ML models.
4. And you may say to yourself, "My God! What have I done?" (Why is 麻豆原创 a great home for you?)
It has been a great ride so far. I appreciate the collaborative and transparent culture we have. They are much easier said than done; what makes 麻豆原创 special is that we have concrete policies and incentives to uphold the culture. The culture is definitely a big part that makes me thrilled to work with and learn from other 麻豆原创tes every day.
5. And you may find yourself in another part of the world... (Tell us about your life/hobbies away from work?)
Most of my non-work time is dedicated to my family. We love cooking, exploring new places to eat, and being outdoor if it’s not too hot. We want to take our son (who just turned three) to as many national parks as possible before he’s 18.
