I was a data scientist at NASA. Here are 5 things to know before you enter the field as it evolves with AI.
Data science is considered a valuable and in-demand skill set across various industries. The demand for data scientists continues to grow despite budget constraints in certain sectors. Chris Mattmann highlights the resilience of data science in the face of budget pauses and reductions. The field of data science offers opportunities for professionals to navigate uncertain economic conditions. Mattmann's perspective sheds light on the enduring importance of data science in today's workforce.
Chris Mattmann
Chris Mattmann worked in data science at NASA for nearly 24 years. He shares the five warnings he'd give others who want to break into the field. Mattmann emphasizes the importance of discipline knowledge, a supportive network, and adapting to AI. This as-told-to essay is based on a conversation with Chris Mattmann, a 44-year-old data scientist from La Canada Flintridge, California, who previously served as NASA Jet Propulsion Laboratory's chief technology and innovation officer and division manager of artificial intelligence, analytics, and innovation organization. Mattmann spent nearly 24 years at NASA before joining UCLA in June 2024 as chief data and artificial intelligence officer. The following has been edited for length and clarity.
Getting Started in Data Science
I got started in data science long before it was even known as "data science." When I studied at the University of Southern California from 1998 to 2007, I worked on data architecture, data engineering, databases, and data systems. My biggest interest was how they were all interconnected.
I started working at NASA as an academic part-time employee in January 2001. Soon after, I was hired full time as a data engineer and software engineer. I moved up at NASA's Jet Propulsion Laboratory (JPL) by working on missions, and had my big break while working on the Orbiting Carbon Observatory Mission, a next-generation earth science instrument. I became JPL's chief technology and innovation officer in 2020.
Five Warnings for Breaking into Data Science
- Study the discipline or data field that you'll be working in.
- Early in your career, get some experience with data science and AI operations.
- To succeed in data science, prepare to be considered "the help" rather than the person driving the domain.
- Build a network of friends to support you through your data science and AI journey.
- AI will change the field so much that software engineering will no longer be as important.
When I entered the industry, I had a lot of training in software development and engineering. I recommend that folks get a discipline science degree rather than a computer or software degree.
For me, the sweet spot is to carefully navigate both data research and operations; don't just hide in the research domain.
The biggest challenges were preparing myself not to be in the lead, spending time working in the background on data analysis.
Community in data science is so important. Having a support network helps to avoid boredom and burnout.
AI is data-hungry for fuel, and understanding math, statistics, and how to evaluate data science and AI will be much more important than building it.
HONESTAI ANALYSIS
Despite the recent DOGE cuts, I wouldn't mind being a data scientist at NASA now. Data science is one of the few fields resilient to the current federal budget pauses and reductions. Being a data scientist positions you well, given this new government direction.
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