In the Stars: Intuit Data Scientist’s Career Start in Astrophysics
A sibling of small business owners shares how she went from PhD in astrophysics to working with data and cloud for the tax giant.
by Maryann Jones Thompson
In 2020, The Data Standard community was formed to build a new community for leading data scientists to come together and share what they’re working on and get input from others.
This community includes people who are like Nhung Ho, director of data science at Intuit, who shared her experiences recently with The Data Standard:
Sometimes a light bulb goes off and your whole life changes. For Ho, it was a moment when she was using an app to manage personal data. She had worked toward a PhD in astrophysics, but when it came time to hit the job market, she recalled that moment with personal data and took a turn into data science instead. She shared her journey into this career with The Data Standard, as well as best practices from a six-year cloud migration project that she discussed at the Women in Data Science conference in March. Below is part one of the conversation. (Read part two, Case Study: Intuit’s 4 Learnings in Cloud Migrations)
How did you move from being an astrophysicist to a data scientist at Intuit?
It’s kind of a funny story. When I was starting my PhD work, I realized that for the first time in my life, I had no money at the end of the month and I didn’t know why. In all my years in the American educational system, no one had taught me how to make a budget!
So I Googled “budget tracker” and Intuit’s Mint.com came up. I loved the way it automatically connected with my bank account, categorized expenses, and offered a budget. I had to correct some of its categorizations but overall, it was great. But when I went into Mint the next month — and the following one — I had to reteach it all the corrections I had taught it the prior month. It was like all the input I was giving it was going into a black hole — and since I’m an astrophysicist, that wasn’t great. I ended up abandoning the product.
A couple years later when I hit the job market, I heard about an opportunity at Intuit and realized they were the makers of Mint. What I didn’t realize was that after 30 years in business, the company was starting a new phase of leveraging AI to use the financial data provided by its millions of users to build better products. So I asked if I could help fix Mint, they said, “Yes,” and I said “Sign me up!”
What exactly is your data science role at Intuit?
I lead our machine learning team and its efforts to build better products for our small business customers — and that’s personal for me. I’m actually the 11th child in a family of 11 kids and half of my siblings are small business owners. Being a statistician, I know that half of small businesses fail in first five years so I want to use my skillset to help Intuit build products that’ll help my siblings and all our entrepreneurial customers to go beyond surviving to thriving and prospering in the future.
What role does data science play in that effort?
Well, finance is personal and that makes it a complicated problem. My own personal finances flummoxed me as a PhD student and small businesses have it much tougher. It is a system that has complications that won’t be solved with a lot of data and a laptop. You need massive compute. That’s why six years ago Intuit decided to move into the cloud and begin its AI-driven transformation.
I have to admit I didn’t fully understand what was needed to move to the cloud. I envisioned snapping our fingers and it was done! It was actually a really long journey: You’re moving from working on your laptop or an on-premise data center to completely changing your workflow in the cloud. And you don’t just move your data but your applications, your services and you need to make sure the cloud environment you’re moving to is completely secure as well. I learned a lot of lessons as we migrated to the cloud.
Finally, how do you think data scientists can advance their role internally?
Well, I have had some data scientists ask me, “Why do we care about all these cloud migration considerations?” The short answer is that I believe we are better data scientists if we understand the end-to-end flow of our work — not just the engineering aspect but how our results show up to the business end users.
I would highly recommend for today’s data scientists to think of themselves not as a person who builds algorithms and “throws them over the wall” but as a strategic leader who is involved in the data science process from start to finish.
By being involved in architecting the “big picture,” our cloud-powered data science allowed Intuit to deliver product features and solve customer problems that were thought to be unsolvable when I joined the company just six years ago.
About the author: Maryann Jones Thompson is a San Francisco Bay Area writer covering the business of innovation and the world of travel. Contact her at maryannjonesthompson@gmail.com.
