Headline Partner
Describe your career to date
I serve as chief decision scientist for Google Cloud, where I help teams unlock the power and beauty in their data by helping them take data science projects from conception to completion.
I sometimes joke that I’m a recovering statistician. Throughout my formal education, I’ve focused on decision-making from every perspective. To me, data is beautiful, but decision-making is important. And when we put data and decision-making together, it creates something extremely powerful, something that can be used to drive business success and to make the world a better place. So, despite having studied statistics both in college and grad school, putting me in the statistician bucket wouldn’t be a fair characterization. What I’m passionate about is decision-making in all its aspects and statistics is just a tiny slice of that. That’s why I also earned degrees in economics, psychology and neuroscience (and I’m always learning as much as I can about other perspectives on decision-making).
I worked throughout my studies, always in something related to data or teaching or both. I’ve been a lecturer, an analyst, a clinical trials coordinator, a data project manager, a statistical consultant, an economic analyst, a high school math teacher and a data collection agent.
When I joined Google nearly a decade ago, I started out as a statistician in our research and machine intelligence group. I quickly realized that there was a lot of great algorithms research, but much less research into how to apply those algorithms effectively – how to stand on the shoulders of giants and make good use of the tools coming out of algorithms research. I want to make sure those beautiful inventions actually get used safely and effectively to make dreams of yesterday the realities of tomorrow. I’m also deeply motivated to help researchers feel like the fruits of their labor are useful outside their academic community. It’s a great feeling when your research gets picked up and applied to making the world brighter.
What stage has your organization reached on its data maturity journey?
As the place that pioneered so many fundamental innovations that data professionals take for granted these days, from MapReduce to transformers, Google might be described as pushing the envelope on data.
What challenges do you see for data in the year ahead that will have an impact on your organization and on the industry as a whole?
AI-based productivity tools and data-fueled products are capturing the public imagination like never before, so it’s reasonable to be concerned that there might be a bumpy learning period as people’s expectations adjust from the AI they’ve read about in science fiction to the reality of useful-but-limited tools that are only as good as the quality of the data they’re based on. I’m concerned that users will expect every AI product to be flawless and leave themselves vulnerable to unanticipated mistakes. Whether we like it or not, we’re all embarking on a society-wide crash course in both the potential and the limitations of data.
Have you set out a vision for data? If so, what is it aiming for and does it embrace the whole organization or just the data function?
Of course! I’ve aimed higher than a vision just for our whole organization and partnered with Google’s People + AI Research (PAIR) team on the data cards playbook, a series of guides and workshops to help train the broader community in data design, data transparency, data quality and data documentation best practices. I’m so proud of our work and I’m thrilled those materials are freely available for everyone’s benefit, though there’s still so much to learn. I hope that the broader community of data professionals will keep sharing what we discover as we all continue on this journey together.
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