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.
Alberto Rey is head of data science at easyJet, where he leads easyJet’s efforts to adopt advance analytics within different areas of the business. Alberto’s background is in air transport and economics, and he has more than 15 years’ experience in the air travel industry. Alberto started his career in advanced analytics as a member of the pricing and revenue management team at easyJet, working in the development of one of the most advanced pricing engines within the industry, where his team pioneered the implementation of machine learning techniques to drive pricing. He holds an MSc in data mining and an MBA from Cranfield University.
Grigorios Mingas is a data scientist in easyJet, where he applies machine learning and statistical techniques to tackle a big variety of business problems and drive profits and savings. He enjoys working with customers and contributing to the evolution and growth of his team and has an interdisciplinary background in Bayesian modeling and parallel computing. Grigorios holds a PhD in electronic and electrical engineering from Imperial College.
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