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Title & Speakers Event
dr dimitra mimie liotsiou – Senior Research Data Scientist @ dunnhumby

It is well known in data science that ‘correlation is not causation’. However, standard data science and machine learning methods are about correlation, not causation. Therefore, to answer questions about cause and effect (e.g. about measuring impacts, uplift, or about why a KPI moved), which are central to many data science projects across sectors, a new, causal, approach is needed, of which the standard data science methods are just one part. In this talk, I will show how we are using the new science of graphical causal inference at dunnhumby to answer cause and effect questions in retail, and I will share key tips and learnings.

causal inference graphical causal inference retail data science
ella johnson watts – Staff Data Scientist @ Deliveroo

Over the last decade, Deliveroo has built a culture of running high-quality experiments. This talk reflects on Deliveroo’s experimentation journey, from the initial vision to what we have achieved so far with some key lessons for low-cost democratised experimentation at-scale.

experimentation a/b testing Data Science
lydia monnington – Data Science Lead @ Google Deepmind

Level up your SQL knowledge to deliver results in real world situations. I’ll cover:

  • Window functions, what makes them great and an example of how to use them to create sessions.
  • Arrays, why data engineers use them and how to access data stored in them easily.
SQL window functions arrays
susan blaszczak – Staff Data Scientist @ Deliveroo

Fraud prevention is a significant challenge for a platform like Deliveroo and this talk is all about how data science can help. I’ll talk about some of the key fraudulent behaviours we try to tackle and dive into the tricky parts – balancing stringent fraud prevention measures with a seamless user experience and the inherent difficulty in establishing ground truth for fraudulent behaviours.

fraud prevention Cyber Security machine learning
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