Interview with Ganna Pogrebna, Lead for Behavioral Data Science, Alan Turing Institute



Ganna Pogrebna is a researcher at The Alan Turing Institute . She hosts a behavioural data science podcast and has recently been nominated as one of the AI Time Journal inspiring  data scientists to follow in 2020 .

We thank Ganna for taking part in the  Data Science Interview Series 2020  and sharing several insightful reflections from her experience, including:

The importance of talking with people from different fields to come up with out-of-the-box solutions.
Her thoughts on the diversity and inclusivity of the data science field.
Her activities as a podcast host.

1. At what point did you realize that you wanted to pursue a career in data science, and how did you get into it?

Unlike many people, I got into data science by complete accident. I started my career as a decision theorist, working on quantitative models of human behavior. I worked in many different universities including Columbia University in the City of New York (USA), University of Warwick (UK), Humboldt University (Germany), etc.

My initial work was in behavioural science rather than data science. Much of my work was about writing mathematical models trying to predict human behavior and then testing these models in the laboratory. The way this worked was I would write a model of human decision making, and then would invite study participants to the laboratory, where they would be making a series of decisions. I would then use the data from those laboratory sessions to see whether the model worked or not. If it did not work – I would write a different model.

While it was exciting to work on predicting human behavior, the laboratory research usually involved several hundred participants; and I always wanted to check whether my theories would work at scale and would deliver value in the real world.

So, in 2013 I managed to get a job at the Warwick Manufacturing Group (University of Warwick), where I could work with businesses in the UK on many projects in consumer choice, digital transformation, and AI using large-scale datasets. My first project looked at using smart-home sensor data to predict consumer decisions. That project changed my life and I knew from that moment on that data science, and, specifically, behavioural data science, is what I wanted to do.

2. How is data science used to create value in your current project(s)?

Much of my work is about understanding people’s preferences. Data science creates value in my projects in 3 ways:

(1) As I work on hybrid models between decision theory and data science (such as, for example, Anthropomorphic Learning modelling approach, which I have been developing for the last 18 months), data science allows me to develop new knowledge.

(2) Data science allows me to solve problems at scale – for example, recently my team has made significant progress in making suggestion systems more...

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