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Gousto: Showcasing Irene, Senior Data Scientist

Category: Gender Balance, International Women’s Day, Careers in Digital Technology, Female role models, Gousto Tech, Senior Data Scientists, Careers in Gousto Tech, Software Engineers, UX Designers, Data Analysts, Working in Tech, Females in Technology

gender focus

It doesn’t need to be International Women’s Day for us to shout about some of the inspiring women we have in Gousto Tech.



We caught up with Irene Iriarte Carretero, one of our Senior Data Scientists to find out what being a data scientist actually entails, and why she chose to work in data to begin with. Irene also shares some great practical tips on how to get started in data and misconceptions people have about working in the tech industry.


Tell us about your role at Gousto

I currently work as a Senior Data Scientist within the Menu tribe, where we focus on creating awesome recipes, menus and digital experience for our customers. As Data Scientists, we use data to build products to help us make better decisions. For example, we are working on a recipe development algorithm that can help recipe developers determine what kind of new recipes we should be creating so that we can build a library with a great mix of different types of recipes.


How has your role changed since joining Gousto?

I joined Gousto in April 2016 when it was much smaller than it is now, which means a lot has changed in these 5 years! It was my first job after uni and I joined as a Data Analyst. Then I got the opportunity to move horizontally and focus more on Data Science. As Gousto scaled, I also got the chance to grow and learn as well as having more responsibility. However, the biggest change is definitely that I have now become a manager and have an awesome team! This means that I am not as hands on as I used to be, but I am loving the new challenges.


What are some of the exciting projects you’re currently working on?

One of the most exciting things is making improvements to our menu planning algorithm! It looks at all the recipes that we have available in our library and decides on the best possible mix to put together for a weekly menu — making sure that it is both varied, so that it has something for every customer, and that it meets all the operational constraints we have as a business. This problem may sound quite simple but there are many things that need to be considered all at once, which means that Data Science can really help make the problem easier!

What influenced you to work in the field of data science and what is it that you love about working in this field?

Before joining Gousto I did a PhD modelling water molecules and I realised that my favourite days were those where I was just playing around with the data that I got from simulations. That is what made me look for jobs in the data field when I finished.

Now that I’ve got more experience of working with data in a commercial setting, there are many things I love about it. First of all, it’s the real impact that we can have with our algorithms — one example being our recommendation algorithm, which hopefully makes it easier for our customers to find recipes they love. I also really enjoy the challenge of trying to apply data science techniques to very abstract problems, such as how different customers understand recipe variety. It pushes us to think creatively about problems and how we go about validating our solutions.

Finally, one of the best bits about my job is the real cross-functional nature of our teams. We work very closely with Gousto’s amazing Food team, Software Engineers, Product Managers, UX Designers, Data Analysts and the list goes on. Everyone brings a different perspective and together we manage to tackle problems we would not be able to solve separately.


Data has traditionally been a male dominated industry, how have you seen this change and what has the gender balance been like in the companies you’ve worked at?

During my PhD I was very lucky that my research group had a great gender split but it was definitely a different story in the field as a whole, so the imbalance would become more apparent when going to external conferences.
Since then, I have only ever worked for Gousto where the balance has varied through the time that I’ve been here. For example in Data Science we currently sit at 25% of women in the team — but we are always looking for ways to get a more diverse pipeline!

What advice/tips do you have for women who want to work in the field of data science?

My main advice is that if anyone is interested, they should absolutely go for it and not be put off by any scary stories! While it’s true that you do need technical ability, Data Science is really all about understanding and solving problems — so if you enjoy doing that, that is a great place to start.
I would also recommend that they join meetups or communities and see what type of problems people are working on to get a real feel of what they might end up doing in their day to day plus they are a great way to meet people who might be able to help you.
Finally — get stuck in! While doing online courses is great, I really believe that it’s when you try to solve a real problem that you learn the most. On top of that, it really sets you apart in interviews if you are able to talk through a real situation that you have solved using data — no matter how small the problem may seem.


What are the common misconceptions women have about the technology industry?

Two main ones come to mind — the first one is that if you work in Tech you basically sit with your headphones on and code away with little to no interaction with others. That could not be further from the truth! No matter the role, working in Tech requires a lot of understanding of problems and working with others to figure things out.

The other one is that people may not be aware of the large number of different roles that are available within the Tech industry (I know I definitely wasn’t aware!) which can suit many different skills and preferences.

What are the big challenges you see facing data science in the near future?

In my opinion, the biggest challenge is making sure that Data Science algorithms are as interpretable and explainable as possible. Being able to understand why decisions are being taken will ensure accountability in this line of work and avoid potentially nasty problems. This will also have the benefit of bringing algorithms closer to the rest of the business.

And lastly, hopefully an easy one, what are your hobbies and things that you like to do outside of work?

Lots of things! I love food, so I spend a lot of my time looking for nice restaurants and watching every possible Masterchef available on TV. I have also gotten very into knitting these past few years, though everything I make is oddly shaped. However with knitting it’s definitely about the journey and not the destination for me. The last thing is doing sports at home. I am constantly amazed by the amount of available oddly specific (20 minute standing abs and arms!) workouts on YouTube — changing it up keeps me entertained.


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