Gousto is a UK-based online retailer that delivers recipe kit boxes with exact quantities of fresh ingredients and step-by-step instructions. Offering deliveries 7 days a week and weekly menus of 22 recipes, we are market leaders in terms of choice. This means that it is vital to our business that we understand how the recipes we offer and the customers who buy them are connected.
Due to the subjective nature of the matter, one of our main challenges was finding a way to measure the similarity between different recipes. In order to do this, we implemented a recipe and ingredient ontology in Neo4j, which allowed us to understand and explore our recipes from many different points of view. This tool then allowed us to create a data-driven menu planning process, which ensures that we are offering enough variety to our customers, and a content-based recommendation system, leading to a more personalised customer experience.
I am a London-based data scientist working for Gousto, a recipe kit delivery company. Originally from Spain, I did my undergraduate studies in Chemistry at Imperial College London and then went on to do a Computational Chemistry PhD studying nanoscale heat transport in ice. This highlighted my love for technical problem solving and I therefore decided to pursue a career in data once out of university. Finding a company that allowed me to work with data and food (the dream!), I did not hesitate. While still fairly junior, I have already had the chance to work with and explore different tools, including graph databases!