DSF Meetup with Depop
7 Curtain Rd, Shoreditch, London EC2A 3LT, UK
Join Data Science Festival - London in partnership with Depop August 14th, for an evening of machine learning and latest framework to production ML systems.
Due to the popularity of Data Science Festival events, we are now allocating event tickets via a random ballot. Registering here enters you into the ticket ballot for the Data Science Festival Event at Depop on August 14th 2019, the ballot will be drawn on August 9th 2019. Those randomly selected will then be e-mailed a Universe ticket for the event, with the joining details.
If you get an allocated Universe ticket, please bring a copy of your paper ticket or your ticket on your phone to the event to check in with your QR code. Tickets are non-transferable.
PLEASE NOTE REGISTERING ON MEETUP DOES NOT GUARANTEE YOU ENTRY TO THIS EVENT.
6:00pm Doors open
6:30pm: Clemence Burnichon
7:00pm: Break (pizza provided)
7:30pm: Amy Monkhouse
8:00pm: Paolo Turati
9:00pm: Leave the building
Clemence Burnichon - Productionising machine learning with spark jobs
Summary: At Depop, we strongly believe that ML deliver value when it is available to all and autonomous. During this talk, I will present our latest framework to production ML systems using spark job and standardised jobs.
Bio: I joined Depop a year and a half ago as a Senior Data Scientist and I am now leading the machine learning effort across Depop. Our mission is to generate knowledge and capabilities to improve our buyer's and seller's experience with Depop. During my 6 years as a data scientist, I have acquired experience in multiple ML fields such as computer vision, recommendation engine and NLP. Prior to Depop, I worked Net-a-porter and Sainsbury’s.
Amy Monkhouse - Using machine learning to protect our users
Summary: Depop has nearly 15 million users, but just like any other online market not all of them are legitimate. A common technique that scammers try to use on our users is to trick them into thinking they want to purchase an item, then moving the conversation out of the app where we can no longer detect any wrongdoings. In this talk, I will discuss the ways we build models to adapt to the specific behaviours scammers use on Depop so that we can automatically ban them with confidence to protect our users.
Bio: I joined Depop just under a year ago as a Junior Data Scientist after graduating from The University of Edinburgh with an MSc in speech and language processing. Since joining, I have used machine learning to allow the business to better understand our inventory and help improve our users’ experiences in the app
Paolo Turati - DXS: the metric for Digital Experience
Summary: While traditional web analytics tools analyse customer experience on websites by tracking metrics such as visited pages or time spent on website, at Decibel we aggregate those simple metrics (plus others) to surface specific behaviours within the user activity. On top of these behaviours we then built more advanced features which describe the Customer Experience at a global level, which are then summarised by the DXS (Digital Experience Score).
Bio: I joined Decibel over 3 years ago as Data Scientist, working on Digital Experience, building Digital Behaviours and developing the Digital Experience Score (DXS). As the team grew I switched to a more operational role and I now manage the Data Science team, leading projects on real-time applications, ML algorithm for decision-making problems, fraud/anomaly detection and NLP classification.
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