DSF Meetup With River Island

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Event

DSF Meetup with River Island

5 September 2019

London

Added 01-Aug-2019

SCHEDULE

6:15: Doors open
6:45: Martin Speed and Gareth Jones
7:05: Sophie Shawdon
7:25: Virginie Bonnefond
7:45: Michael Leznik
8:05: Networking, food and drinks
8:45-9:00: Leave the building

Martin Speed - Safety and Loss Programs Manager at River Island

Gareth Jones - Safety and Loss Analyst at River Island

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.

Sophie Shawdon - Senior Data Analyst at ClearScore

Summary: Talk Data To Me: Using Machine Learning to Tell Customer Stories. As businesses become more number-driven, how do you ensure that the qualitative data - and in particular what your users are telling you - does not get left behind? In this talk, Sophie will talk through ClearScore's recent work on using machine learning to better understand its customers; and what we learned; and why there's so much value in 'dormant' data.

Virginie Bonnefond - Data Scientist at Hummingbird Technologies

Summary: The recent progress in deep learning is slowly but consistently shifting the paradigm that was followed in remote sensing data processing over the last decades. AI applications are starting to become available for a wide range of applications, from on-board data reduction to fine-grained analytics. Agriculture is one of the areas with the largest social impact of this technological revolution, through a two-pronged strategy. Firstly, AI technology is used to replace the typical flat-rate applications of chemicals with targeted applications only to regions of need, thus alleviating the environmental stress caused by intensive farming practices. Secondly, AI is used to substitute agronomic input across the season, something particularly meaningful in parts of the world where access to agronomical input is limited. In this talk, we are going to present in detail the related topics, while also discussing the technology bottlenecks, limitations and future directions.

Michael Leznik - Head of Data Science at Product Madness

Summary: Bayesian Decision making becoming the widely adopted methodology of choice in the industry. Ability to make your conclusions based on probability distributions instead of arbitrary binary p-values seems to be much preferable when it comes to decision making. The methodology allows to accept null values and estimate statistical power. Using non-parametric Bayesian helps to deal with non-standard models and avoid reduction of statistical power when the probability distribution isn’t normal. The latter is a major drawback when using regular t-tests. Non-binary; significant vs non-significant conclusions provide decision-makers with a plethora of outcome and wide field of possibilities.

 

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