Tandem Cats: Dani Navas

A celebration of Tandem culture taking pride in our talent and sharing our expertise with the world

Be brave, be simple, be proactive, together!

You might be familiar with one of the most successful animated series from the ’80s: ThunderCats. Their mission was to flee their home world to find the legendary mystic Sword of Omens that holds the Eye of Thundera, the source of their power.

We believe everyone has a superpower, so we’ve been inspired by the ThunderCats to create the Tandem-Cats, a blog series dedicated to telling the story behind each of our team members, shedding light on Tandem’s talents’ expertise.

Every week we get together at Marketing Press corner to interview one of our nominated team members, where we find out about their superpower and how they work their magic on a daily basis at Tandem HQ.

Meet the Data Scientist: Daniel Navas

dani-navas

What led you to become a Data Scientist at Tandem?

I'm a trained mechanical engineer and worked in Spain as an airport planner. I did that for two years but it was a bit too old tech for me, so I decided to move on and get into data science. I went to Edinburgh to do my degree and Masters. I then joined a food tech-startup in Paris and after three months on a contract basis, I started to look for jobs in London and found Tandem!

What are the main challenges and pain points you overcome on a daily-basis?

We are a growing company, which means we are continuously evolving. We work for different stakeholders with different objectives and our role is to find a solution to global problems within the organization.

Who’s your tech hero?

I like this guy called DJ Patil—he used to be a data scientist for the Obama administration. He currently works on an AI-based healthcare company and is a big data science ethics advocate.

Tell me a little bit about your work. What makes your expertise so unique?

What our data science team is trying to achieve is combining our customer's data with our knowledgeable company expertise. Therefore, hypotheses based on experience are tested and proven by data.

In your own words, what's the role data plays at Tandem?

We are a data-driven company; the features of our products take into account customer insights and data. The safe to save function, for example, looks into your spending behaviour to tell you how much you can save on a weekly/monthly basis so that we can provide a custom-made tool.

In your opinion, what's the most crucial difference between the new challenger banks like Tandem and traditional banking?

As a foreigner I believe it is tough to have a history with a traditional bank. Tandem looks into alternatives to be able to prove customers’ credibility such as credit score and spending habits. This is a significant differentiator.

How is Tandem unique in comparison to the main challenger banks?

Tandem builds purpose-driven products that will improve our customer's lives. Our motto is to be as fair as possible and this is behind every financial solution we build.

What’s your favourite product/service that Tandem offers?

Autosavings! A lot of work has been done to create a tool that calculates how much people can spend and offer alternatives to optimise their saving possibilities. Giving our customers the power to set up rules that will increase their amount of savings is one of the Tandem app’s unique features.

Despite being extremely data and results driven, I know Tandem can be a very playful and outgoing place to work. How would you define the brand culture?

I think Tandem allows us to be playful and outgoing. One thing that I love about our culture is that we have activities like the table tennis tournament. The whole objective of that wasn't merely to play but engage with our co-workers. We are trying to create a flat company structure where everyone is trying to achieve the same goals together, so we can easily reach out to each other and work collaboratively.

I know you've been working for Tandem for a few months. Would you be able to tell me what the main highlight of your journey so far has been?

The first model I developed was a fraud detection machine learning tool that aims to help our fraud team to organize their work better and tackle fraudulent activities. The model has had a huge impact, which is extremely rewarding.

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