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Location

1 Curtain Place, London EC2A 3AN

When

6:00 PM - 9:00 PM



DSF Meetup with River Island

@
6:00 pm-
9:00 pm
September 5th , 2019, Thursday
Location:
Type:
Join Data Science Festival – London in partnership with River Island September 5th, for an evening of 4 lightning talks.
 
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 River Island on September 5th 2019, the ballot will be drawn on September 2nd 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.
Please click here to apply for a ticket: GET TICKETS
SCHEDULE
 
6:15: Doors open
6:45: Martin Speed and Gareth Jones – Data Science to support store process: insight from an in-store replenishment model
7:05: TBC
7:20: Break
7:45: TBC
8:05: TBC
8:20: Networking
8:45-9:00: Leave the building

Martin Speed – Safety and Loss Programs Manager at River Island

Bio: Martin’s PhD is in criminology and he built his experience building crime risk models and researching for the sentencing advisory council, the Home Office, and the Ministry of Justice. 

Gareth Jones – Safety and Loss Analyst at River Island

Bio: Gareth has a Master’s in Maths and just completed a Master’s in Data Science. He has been working as a senior analyst at River Island for 5 years and his previous modelling projects included predicting Stock Loss at stores. With Martin he has formed the new River Island Data Science team.

Summary: as a recently created data science team, our focus is to provide actionable insight quickly.  Size gaps on the shop floor that do not currently get replenished represent a £2.5m opportunity and our RFID data can now identify exactly where they occur.  Modelling the factors associated with the gaps occurring gave insight into how the issue could be addressed.