How We Work in GoustoTech - Part 1 - Team Structure

After joining Gousto as Head of Engineering in September 2017 I’ve spent a lot of time interviewing candidates as we look to scale out our engineering team. One of the most common questions I’m asked is how we structure the team here at Gousto. This is the first in a series of posts looking at ‘how’ (and most importantly ‘why’) we structure our engineering team this way.

At Gousto we actually split our Data Science team from our Software Engineering team, but work amazing closely. I’ve included both in this article so you can see how they fit together. At the current time our combined team is around 30 people but this is likely to grow up to around 50 by the end of 2018. This is the first part of our growth as we continue to scale the company and expect this only to continue into 2019.

Keep Reading

Turning Food Waste into a Byte-sized Problem

The Olympic swimming pool; the double-decker bus; the football stadium. Whilst researching stats to include in this post, it saddened me to discover that these are all units commonly used to quantify the UK’s food waste. As for where it’s all coming from, studies (for example, this study by WRAP) suggest that retail and household food waste accounts for around three quarters of the UK’s overall post-farm-gate total. Given Gousto’s rapid growth, it is now more important than ever for us to consider our impact in terms of food waste. So what exactly are we doing to help? In this post, I will attempt to answer that question, focusing particularly on our strong investment in data science and analytics.

Keep Reading

Data Science for Dinner at the University of Cambridge

The UK has a young, but fast-growing data science community, as evidenced by the growing number of regular data science meet-ups and companies offering training programmes like ASI or S2DS. As a data science team we are keen to learn from fellow data scientists, as well as to showcase our work and the impact it has had on the business. Giving talks at universities is one way in which connect with the community, while improving our chances to grow our team with the UK’s most promising new data scientists. Recently, we combined a team off-site in Cambridge with a workshop in partnership with the Cambridge University Data Science Society. You can find the slides we used for the event in this post, along with some learnings we took away as a team.

Keep Reading

12 Weeks as a Mushroom

A look back at my 12 weeks interning in Gousto’s Mobile Apps team

Hi, I’m Nik! I’m a 19-something Computer Science student at Imperial College
London and just finished my summer internship at Gousto, working in the
iOS/Mobile team called ‘Mushrooms’ (now the title makes sense) 😄.

This is my first internship and it was a super exciting opportunity to work
in a tech team at a company on a real product. That’s right, some of what
I’ve worked on is now out for the world to see and is hopefully benefitting
the business as well. It was also my first time participating in code
reviews, both having my code be reviewed and being the one who reviews.

I’ll try to catalogue what I’ve done and what it’s been like working here for
12 weeks in this post.

Keep Reading

An Insight into Gousto’s Data Kitchen

When I started my job at Gousto, I honestly struggled to get my head around how data-driven the company is, with every single team leveraging data in some way. Since joining, we (the analytics team) have undertaken some seriously cool projects across the entire business – a few examples being:

  • Building a predictive model to calculate the likelihood of a customer buying their next Gousto recipe box
  • Analysing customers’ online behaviour to make the website user experience seamless and as easy to use as possible
  • Working closely with Gousto data scientists to optimise the performance of our automated ingredient picking operation

With this in mind, it’s easy for our colleagues to sometimes forget that we don’t have a magical crystal ball to answer their questions. Instead of supernatural powers, we prefer a mixture of SQL and Python and tools like Periscope to solve Gousto’s challenges.

The area of analytics I want to focus on in this blog post is really at the heart of the business and is the main reason I (and probably most of my colleagues) joined Gousto – FOOD. Given the level of investment into data science and analytics elsewhere in the business, it should come as no surprise to learn that we crunch some serious numbers to understand what it is that tickles our customers’ taste buds and keeps them coming back for more. In particular, I will explain how we use data to inform the recipe development process here at Gousto. I’ll then showcase some of our tastiest dishes, before finally revealing some fun facts and interesting stats about our recipe collection.

Keep Reading

Hash-Caching query results in Python

Developing data science products will in most cases require extensive testing and tuning models using historic data. At Gousto this is the case for a number of optimisation and machine learning algorithms powering our operations and marketing efforts. In turn, the majority of these cases will involve large volume data queried from a database. In order to maintain agility in developing your data-driven product the last thing you want is to wait for queries to complete while testing iterations to your code.

To prevent re-running large queries we can construct a simple Python method to cross-reference a hashed SQL query against stored query results saved under hash-filenames. Keep reading to find out how!

Keep Reading

Decyphering recipes

Last month we got the chance to present some of the work we have been doing at a neo4j meetup. In the lightning talk, I explained how neo4j has been helping us with our automatic menu planning process, a tool we use to predict similarity between different recipes. You can see the slides at the bottom!

Our weekly menus are a vital part of our business and we need to make sure we are offering customers balanced menus which give them the choice to pick recipes they really want. Therefore, a lot of thought goes into planning these menus, which need to fulfil several operational constraints and offer a range of ingredients and cuisines. With 22 recipes currently on the menu, the planning was a time consuming task and the process was also not as data-driven as it could be!

Keep Reading

A day in the life of a data scientist

There is no general consensus as to what a data scientist is or how to become one. Is she a statistician, an operations researcher or a broker between data analysts and developers? Perhaps identifying a data scientist’s work by her common tasks is not the best approach. Instead, why not run through a day on the data science team here at Gousto? This post is about a recent trip the team made to the company warehouse in Spalding.

Keep Reading

Gousto use (and love) Snowplow - slides from Snowplow London Meetup

I was presenting our journey of leveraging Snowplow at the 4th Snowplow London Meetup yesterday. I wrote about Snowplow before for those who don’t know what it is.

I was talking about our data infrastructure and use cases of machine learning that have helped us to be the leader in the recipe kit market in terms of the proposition we offer to our customers. Our customers can choose from 22 recipes and get their boxes delivered in 2-3 days from a choose 6 delivery days a week (we don’t deliver on Fridays at the moment).

Keep Reading