Data science survival and growth within the corporate jungle: An easyJet case study

Description

What does it take for a data science team to move from statistics and model design to actually delivering value within a corporate environment, where customers have very different backgrounds and requirements?

With 500,000 flights and over 80 million passengers a year, internal teams specializing in operations, maintenance, scheduling, network expansion, pricing, consumer preferences, marketing and inflight retail, and a growing data science team, easyJet is a representative example of a diverse environment with a lots of opportunities and even more potential traps for aspiring disruptors.

In this challenging environment, business problems require a variety of approaches and techniques to solve, often extending beyond core data science skills (e.g., operations research, web development). At the same time, expert support from IT and other functions is often unavailable. Data scientist turnover can lead to projects being discontinued. Business teams have their own legacy systems that they swear by, and skepticism or resistance to change is common. Business functions are often not fully aware of what they want to achieve when engaging the data science team. Data sources are distributed, isolated, or even completely inaccessible. And bringing models into production can mean different things, and there is often a trade-off between what is useable and what can be created quickly.

Alberto Rey Villaverde and Grigorios Mingas share case studies from easyJet that highlight some unpredictable hurdles related to requirements, data, infrastructure, and deployment and explain how they solved them. You’ll also learn how easyJet has adapted its processes, structure, collaboration practices, and infrastructure in order to avoid many of these in the future.

Topics include:

• Predictive maintenance: Determining optimal replacement times for the thousands of electrical and mechanical components found in an Airbus aircraft
• Slot matching: An interactive tool that matches the commercial schedule with airport slots, given user constraints and costs
• Food waste: Minimizing food waste using machine learning techniques
• OTP forecast: A model to predict when flights arrive on time and inform operations to take preemptive action, using customized visualizations
• Pricing: ML algorithms to optimize pricing decisions and augment the capabilities of human pricing managers