Today on the Data Futurology podcast, we have Ann Sebastian, Senior Data Scientist at Wesfarmers OneDigital, as a guest on the podcast.
As Sebastian says, she is “in the trenches” building data science products. One of her key projects in recent years has been OnePass, a subscription service that provides free delivery and other services across a range of Australia’s top brands.
“It’s an incredible experience to be part of a journey, developing an idea through to proof of concept through to production ideation to a system that is adopted across the organisation,” she said. Through the podcast, Sebastian offers some key insights into that process via some of the projects that she has worked on over the years.
Sebastian also spoke about how data science teams can be built, and how a culture of innovation can be structured within them. For just one example of this that she shares on the podcast, in her current role there is a focus on learning and development, which manifests as 10 per cent of each person’s work time being dedicated to research activities.
For her part, Sebastian is currently using that research time to work on multimodal product classification, she said. “Given the fast-moving nature of retail catalogue, and need for us as a division to form a unified view across all our divisions products, there is business significance for this research project.
“We then have fortnightly quick check ins to discuss the progress on our research projects, and that really helps us to learn from each other. This is one way that data science is embedded into our day to day in a way that makes it more real for us.”
For these insights and more on how data science products are built and evaluated, and how data scientists can be motivated and innovative within their careers, tune in to the full podcast.
Enjoy the show!
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What we discussed:
0:00 Introduction
3:00 Ann talks about her experience and her remit at Wesfarmers
7:44 What are some of the use cases you’re proudest of?
15:12 Ann shares more information about her favourite use case and how it evolved from an idea to the start of the technical work.
17:58 How did you measure business impact?
20:14 How did you operationalize the models?
24:00 Can you describe your current role?
31:23 What’s your advice for people wanting to get into data science?
Quotes:
· Business teams can sometimes view data science as a mythical creature, so I love working with them to demystify data science and achieve business benefits through it.
· The use case that I'm proudest of is the automation of the complaint classification, where we implemented various natural language processing models to predict the category of the complaints using real time models.
