Welcome to another edition of SFTW Convo! This week’s conversation features David Clifford. David is a friend and I had the privilege to work with him at Mineral. David is a data scientist by training.
He was the head of data science at Mineral, has experience working at Metromile (an insurance company), the Climate Corporation (part of Bayer Crop Science), and spent about 10 years at the Australian research organization CSIRO. David got his PhD from the University of Chicago (so we are fellow alums as well!) David currently works as an independent consultant and resides in Ireland.
Summary of the Conversation
In this conversation, David Clifford discusses the intricacies of data science in agriculture, emphasizing the importance of starting with a question and the challenges faced in data quality and bias. He explores the role of AI in agriculture, addressing common misconceptions and the bottlenecks on innovation due to the natural cycles of farming. The discussion highlights the need for effective feedback loops and the potential for AI to enhance agricultural practices while acknowledging the inherent challenges in the industry.
David Clifford discusses various models of innovation in agriculture, comparing the approach of Climate with the ambitious strategies of Mineral. He highlights the importance of understanding customer needs and the specific context of agricultural challenges. The discussion goes into cultural differences in agricultural innovation across the US, and Europe, emphasizing the role of academia in bridging research and commercial applications. David shares insights on the potential impact of AI in agriculture, particularly in cost of production in farming activities, and speculates on future trends in farming, including the challenges of succession planning and the aging farmer demographic.
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A data scientist gets real about AgTech challenges
Rhishi Pethe: People talk about data scientists a lot, but what the hell is a data scientist? Outside of just pure technical skills, what makes for a good data scientist?
David Clifford: A data scientist works comfortably with data in all its different forms. I think the definition of a data scientist has shifted over time. When I began my career, a data scientist was essentially a statistician working with a dataset small enough to fit into a computer’s memory.
Back then, the typical process looked like this: “Here’s a dataset, find something interesting in it.” That was the academic approach I was trained in. But once you enter industry, things change. It becomes more like: “Here’s a dataset and a question, can you answer it?” And as you progress in your career, the framing shifts again: “Here’s a question, how do we generate the right data to answer it?”
At that stage, a data scientist’s job involves gathering the right information, often by connecting the dots across multiple sources. You cycle through a process: clean & structure data, visualize it, build models to capture what those visuals show, go back to restructure the data as your exploration comes together, and repeat. With each loop, you gain a deeper understanding of the problem and refine your sense of what the answer might be, and how to support it with evidence.
