Chris Mattmann spent nearly 24 years at NASA, most recently as chief data and AI officer at UCLA. In this as-told-to essay, he shares the five key pieces of advice he would give to anyone looking to enter the data science field. His career began long before the term “data science” was in common use, giving him a unique perspective on the evolution of the profession and the skills needed to succeed.
1. Study the Discipline
Mattmann firmly believes that aspiring data scientists should prioritize a deep understanding of the discipline or data field they’ll be working in. He suggests that a discipline science degree is more valuable than a computer or software degree. “AI is coming for your software engineering job, but it isn’t the best at discipline sciences,” he explains. He saw firsthand that individuals with backgrounds in earth science, planetary science, and mathematics were often more effective data scientists than those with solely software engineering experience.
2. Get Hands-On Early
Mattmann emphasizes the importance of gaining early experience in both data science and AI operations. He suggests two main entry points: engaging with open-source tools and data, and presenting that work publicly via platforms like GitHub, or working under a mentor through internships to make a publicly reviewable contribution to data. He views operations as a more rewarding path than the often “cutthroat” research and science publishing world.
3. Embrace the Supporting Role
Data scientists often work in the background, enabling others to achieve their goals. Mattmann advises newcomers to prepare to be the “help” rather than always seeking the spotlight. In many cases, data scientists support discipline experts, preparing, analyzing, and working with data. Humility is essential in this realm, especially as data professionals fuel AI’s growth.
4. Build a Supportive Network
Community is critical in data science. Data science often requires a team effort, and a supportive network provides the necessary encouragement. Mattmann underscores the importance of having people who lift you up and are interested in your work. He encourages those feeling isolated to participate in data science competitions, attend meet-ups, and actively build their own network.
5. Adapt to AI’s Impact
AI is transforming the field, and Mattmann urges aspiring data scientists to anticipate these changes. He predicts that AI will handle much of the data analysis in the next five to ten years. Therefore, focus on the skills that will remain crucial: training new AI models and refining data. Additionally, understanding the legal and ethical implications of AI and data models will become more important. The ability to craft compelling data stories and thrive in a supportive role will be valuable.
Despite the evolving landscape, Mattmann remains optimistic about the high demand for data scientists across various sectors, including industry, government, commercial, and academia. Data is the fuel for AI, making data science a profession resilient to change.
