Meet Paloma Rebuelta, Data Science Engineer. We caught up with Paloma ahead of the women in excelling in data event she is hosting alongside Isobel Daley this month. In this interview, we get to know Paloma better as she discusses her experience in data.

Paloma, how did you get into data?

After I finished my civil engineering degree, I realised that the modules that appealed to me most involved coding and technology, but I wasn’t sure which tech roles were right for me. I chose a DevOps graduate scheme at a big consulting firm as it felt like the perfect way to enter the tech industry while learning more about other areas. I worked on some projects alongside data scientists, and I quickly realised that that was what I wanted to do, as it was a mixture of some of my key areas of interest like coding, problem-solving and algorithms. I was able to get a job as a data science engineer at 6point6, which allowed me to develop in data science and use the DevOps skills I learned during my graduate scheme.

Where did your passion for data stem from?

I believe it has always been there. Through high school and university, my favourite module was mathematics. When I started university and learned how to program, I realised how fascinating it was to apply all those concepts to real-world data and identify patterns that could have a real-life impact. The more I learn about it, the more I’m drawn towards it as I learn about new techniques and approaches to complex problems.

What trends do you predict in data this year?

One of the main challenges for data scientists is productionising their work. Many companies are willing to try proofs of concept (POCs) but shy away from investing in fully working products. I believe an increasing number of companies are realising that simply having data scientists working on these POCs will not allow them to harvest the true potential of their data, and there will be a switch towards getting more products across the line. This will introduce a need for data scientists to learn Machine Learning and data operations or companies to hire more data engineers.

The second trend I believe we will see is the increased use of blockchain in data science. Blockchain is getting incredible traction, but most use-cases are only beginning to be explored. In data science, one of the issues that scientists struggle with is erroneous, incomplete or duplicated data. The process of data preparation can be time-consuming and if done wrong, can lead to poor and sometimes misleading results. This is where blockchain can help. Blockchain is a decentralised ledger that records transactions. This data is validated thanks to cryptography, which is then used to create a distributed trust network, making it impossible for data to be manipulated. Its decentralised nature will also help with privacy and security, as the data will no longer need to be stored in a centralised data centre where it is susceptible to hacker attacks. Instead, it will remain on an individual scale, making it significantly harder for hackers to access large amounts of data.

What advice would you give to women who want to pursue a career in data?

My advice would be to go for it! Identify an area you want to learn more about and do a course on it (there are endless online resources) to see if it’s something you want to do for a living. Contributing to open-source projects is also a great way to gauge whether it’s the right fit for you or if maybe a different role in data is a better choice. The industry consists of people from different backgrounds and interests, so it is never too late to get started.

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