People to Watch 2019
General Manager of Machine Learning
Hilary Mason is the General Manager for Machine Learning at Cloudera. Previously, she founded Fast Forward Labs, an applied machine learning research and advisory company, which was acquired by Cloudera in 2017. Hilary is the Data Scientist in Residence at Accel Partners, and is on the board of the Anita Borg Institute. Previously, she co-founded HackNY.org, a non-profit that helps engineering students find opportunities in New York’s creative technical economy, served on Mayer Bloomberg’s Technology Advisory Council, and was the Chief Scientist at Bitly.
Datanami: Hilary, you’ve been involved in data science since before it was called “data science.” How has the practice changed over time? Is it easier or harder to be a data scientist today?
Hilary Mason: The progress in data science tools means that it’s easier to practice data science today than it has ever been before. Between the availability of data science platforms to ease workflows and robust open-source libraries to simplify the use of many algorithms, a single person can work more productively today than at any time in the past.
On another level, while “data science” has evolved into a credible job title, there’s still quite a bit of confusion in the industry about who data scientists are and what they do. So while we definitely have more work to do, there’s never been a better time to be a data scientist.
Datanami: Cloudera is merging with Hortonworks, which had been its closest rival for years. How will the addition of Hortonworks employees bolster Cloudera’s vision for AI and machine learning?
The combined company has a ton of fantastic talent. It’s like instantly doubling the number of smart people you get to work with. I’m excited to focus as much of that energy into our machine learning and AI products and services as possible.
Datanami: AI investments continued to go up, but there’s growing concerns over ethics and privacy. What responsibility do data science and app developers have to ensure things are built the right way?
I believe that you are responsible for the impact of the things that you create. As a technologist in this space, you’re responsible for asking questions about the potential impact the systems that you contribute to might have on people. Now is the time for these sorts of questions to become a standard of data science and AI practice.
In our Cloudera Fast Forward Labs research group, we’ve included a chapter on ethics in every report we’ve written, showing how one might think about ethics with respect to a given machine learning capability. This has led to many productive conversations.
Datanami: You were a data hacker before being a data hacker was cool. What do you miss most about the old days of data science?
Being a data hacker over a decade ago was a bit like being in a fun club. People were always surprised to see what you could do with a bit of cleverness!
I once did an analysis of over 1,000 chocolate chip cookie recipes in an attempt to model the perfect one. I miss some of the freedom and playfulness of the community at that time. We were just at the beginning of so many things and were still figuring out what new data sources could tell us, without an expectation that the work would lead directly to financial ROI.
Datanami: Outside of the professional sphere, what can you share about yourself that your colleagues might be surprised to learn – any unique hobbies or stories?
I have many stories, but I prefer to share them personally over coffee or ice cream. It always surprises me the odd and interesting places my work and interests have taken me to.
It might surprise you that my first job was being a ski instructor at our little local ski area when I was a teenager, teaching mostly kids. I’ve always loved teaching.