Analysing and interpreting complex digital data is a skill that is integral to the smooth working of many businesses. Data scientists are expert at this – but the role of the data scientist is more difficult to define.  

As technology has developed and more companies and sectors have employed data scientists, the job has come to encompass many different elements and areas of expertise. Of particular interest to many is artificial intelligence, a technology that is continuing to grow in popularity and for which data scientists are integral. 

As part of my Data Stories series, a set of articles that focus on the careers of data professionals from across the Nordic region, I have spoken to a group of six data scientists to find out about their careers, their advice for others, and how they see the role developing in the future. 

I have learned about their backgrounds, how their career paths evolved and what they enjoy about this complex and fascinating role. 

The Skills of a Data Scientist 

Although data scientists often build models and algorithms, some of the key skills a data scientist must have are a keen analytical mind, curiosity and creativity in order to respond to data and solve complex problems. The education required for the role varies, but will usually require a strong mathematical or natural science basis.  

The need for data scientists has grown drastically over the last decade, and whereas previously they were employed in mainly technology companies, opportunities for data scientists can now be found in a huge variety of sectors. 

“What Drives Me the Most is When You Combine Tech with Actual Value.” 

Stylianos Gisdakis is the Head of Data Science at Anyfin. With expertise in credit modelling, fraud modelling and business performance monitoring, Stylianos has experience in many areas of data science. His current role encompasses credit policies, credit models, image analysis and OCR alongside being a functional monitor for the people on his team. 

His passion in tech is providing value to consumers and businesses.  

“What drives me the most is when you combine tech with actual value. I prefer cases where really nice tech is used to solve actual business problems, and essentially to bring value to the customers. 

This drive to provide value runs through Stylianos’ career. His advice for aspiring data scientists is to learn as much as possible about the company in order to perform as well as you can.  

“I always think that the single most important thing is that you should be able to learn the business you’re operating. Try to understand the technology of the company, try to understand what the company is trying to optimise for, what is the value that the company brings to the customer. 

It is only through a deep understanding of the company and its working processes that a data scientist can work to the best of their ability.   

And once you are able to understand that, then you can use all of your tools, skills and tricks to perform for that company. My advice, and what I have followed is that you should always try to have as much of an understanding as possible about the company. This is essentially how you bring a lot of value.” 

Stylianos’ philosophy is not to dwell on past decisions in his career, and instead to focus on the day to day. 

“I wouldn’t change anything. What I have become is the sum of my successes and my failures. And personally, I’m really happy with my career path so far.” 

“You need to be able to understand the underlying physical or social process.” 

Quirin Hamp is Lead Data Scientist at Stockholm Exergi. Quirin has 15 years of experience in the energy and utility sector, and a PhD in Information Fusion from the University of Freiburg.  

Quirin’s love for data science comes from his intrinsic ambition to create something of value.  

“Tech is something where you can be creative. Basically, that’s what I’m striving for, to create something of value using the advances of technology and infrastructures.” 

For anyone planning a career in data science, education is key. However, because data science is interdisciplinary, Quirin’s advice is to study a major technical field such as computer science, mechanical or electrical engineering, mathematics or physics.  

“Data Science is an interdisciplinary field where programming skills get applied to process data in order to extract some sort of knowledge that is not accessible in a straightforward manner. Therefore, you need to be able to understand the underlying physical, social, or business process.” 

As well as this, it is important to keep learning throughout your career, and keep up to date with relevant research to ensure that you are continually ahead of the curve.  

Quirin sees data science as something that is going to grow in the years to come, with development infrastructures becoming more versatile and user-friendly.  

“I think that companies will more and more understand the value of data science. And they’re going to employ it.” 

The technology will continue to improve, but Quirin warns that although companies will continue to move towards it, they may also become disappointed by the perceived lack of results, and this is something data scientists will need to work around.  

“There will be a lot of companies that go towards that science, but there’s going to be a disillusion phase. Because data science cannot solve everything. In particular, the current expectations on artificial intelligence” might be too high. 

Therefore, it is the responsibility of data scientist to contribute to a common and shared understanding of what the various technologies mean and are capable of until the first real artificial intelligence will be created.  

“Always Think About the Big Picture.” 

Celine Xu is a Data Scientist Lead at Axel Johnson. She and her team create either proof of concept of end-to-end machine learning products to drive business revenue. Previously, Celine worked for Accenture, also in the analytic and data science field. 

For Celine, her passion in technology is allowing people to see the benefits that it can bring to businesses and consumers. 

“I love to help to make the process more effective, and to change people’s minds to make them believe, actually the machine is helping them instead of threatening them.” 

For people who are considering becoming data scientists, Celine recommends keeping an eye on the bigger picture in order to be successful, rather than staying too focused on the statistics themselves. 

“Don’t only focus on the statistic and model itself, the accuracy. Always think about the big picture – how the things can make people’s life easier, or the function you’re supporting can make life easier, and how you can drive the business profitability directly.” 

Looking to the future, it may be the case that programming will become a necessary skill for everyone. For data scientists, it is likely that this will lead to more specialisation.  

“Data scientists will have more jobs and a wider area to work on. The job description will be more detailed and specialised.” 

Essentially, the work covered by the job title ‘data scientist’ will be much wider, but the positions themselves will be much more specialised. This will give people scope to become experts in particular areas. 

“For example, maybe some companies will actually want to recruit someone doing Power BI dashboard, but they call it data scientist. Some companies will actually only need someone doing the AV testing, but they call it a data scientist. And some companies actually only need someone to move the machine from prototype to production, but they still call it data scientist.” 

“The Top Technologies Today Might Not be the Top Technologies in Two or Three Months from Now.” 

Lucas Peinado Bruscato is Lead Data Scientist at Klarna. His career began at a bank in Brazil, where he worked to improve the model and technology processes they had in place. He then moved to various fintech companies. 

For Lucas, one of the most exciting things about data science is the multitude of different ways that value can be provided.  

“I like to see how we are evolving, how we are using technology to build new things and extract value from different opportunities.” 

The process of gathering data and then considering how that can be transformed into something that serves a purpose for a business, and the creativity involved, is what Lucas enjoys most about his role. As with any other role, challenges can come when moving away from completely hands-on to more management.  

“I’m a person that is very driven, in terms of technology, and actually being hands on. When you become a lead data scientist, you start to do more management, or maybe 60 to 70% of my time, I do more management work, and just 30 to 40% of coding. So that’s one challenge, to balance this in a way that is beneficial for me and my team.” 

In order to be successful as a data scientist, it is important to stay curious. This is an extremely fast-paced industry, and the best data scientists are those who enjoy keeping up-to-date with the latest processes. 

“You have to always be reinventing yourself and re learning things. The top technologies today might not be the top technologies in two or three months from now. Sometimes you can see the evolution of tools through time, and that’s quite neat.” 

Looking to the future, challenges are going to come when data scientists are creating solutions that remove the need for them to be on a team. This highlights the importance of reinvention, and not being afraid to move on.  

“In an extreme case, a data scientist was actually working on a certain thing, this person developed the tools and the project that he was working on so much that in the end, he said, okay, they don’t need me anymore here. So basically, the process is working. Maybe you need some fine tuning now and then. But then I can move to a different team and a new project. 

“I Love Being at the Forefront of Technology.” 

Ali Leyani, Chief Data Scientist at Granditude, has a background in theoretical physics and quantum informatics. His trajectory changed from academia when he realised that he wanted to work with something that would be impactful, right now. This led him to machine learning and artificial intelligence, which also just happened to fit in very well with his education.. 

For Ali, the draw of data science is the ability to work with the most modern technology. 

“I love being at the forefront of technology. Just being where the action is and exploring the unknown, because that’s what this field is right now. Business has never been as close to research as it is right now, with artificial intelligence. And there are many consequences of this, this is from Spider Man, you know, with great power comes great responsibility. As a cliche, people say it all the time, but honestly, it should be taken seriously here, because you can do a lot of things with this technology. It can go wrong in so many ways. Most of them are subtle, you won’t even notice it, but they have major impacts on people.  

A key point that comes up regularly during my interviews in this field is the importance of continuing to learn – particularly in such a fast-paced industry. Data science evolves quickly, and it is vital that you keep up with the latest developments. Additionally, Ali values communication skills. 

“You are going to be that bridge from this forefront technology that most people see as some sort of science fiction or don’t understand. You’re going to talk to people who don’t understand technology, but they’re business leaders. As a data scientist, you should be prepared to step up and be the bridge.” 

Ali agrees that data science will split further into technical fields as people become experts in various elements. As well as specialisms in areas such as machine translation, this will also include data scientists who are expert communicators too, who are able to explain the complex technology to stakeholders. There will also be more customer facing data science work. 

“Customer facing data science is going to be more prominent. We’re going to get more and more roles out of it, like ethics officers. More responsible AI, not so tech heavy roles, but more customer impact roles and assessment roles.” 

“You Learn Every Day.” 

Emma Lee Bergström is a Senior Data Scientist at Nordic Entertainment Group. Originally from South Korea, she moved to Australia when she was 20 years old, and to Sweden three years later. She studied statistic and data analysis, statistical and data mining. 

For Emma, who was interested in economics, studying statistics was a good middle ground for someone who had only recently started to learn Swedish and so may have struggled with the lectures and discussions that economics would have required. But along the journey she realized that it was a better choice as she grew to love math and tech, particularly enjoying the constant learning involved. 

“You learn every day. There’s so many new things to learn. So it’s quite challenging, but at the same time you get motivated, and you learn new stuff.” 

For Emma, the key to being a successful data scientist is understanding the bigger picture, rather than focusing soley on the programming. This will lead to a project that hits the targets and achieves goals. 

“It’s really good that you are good at the programming, because you always need that. But as long as you master the basicsI think you can learn the programming as time goes by. The thing is, you need to sit there and think about the holistic view, or what is the thing you’re going to do? What is the purpose? 

As the number of data scientists continues to grow, standing out from the crowd will be important. Emma believes that this will come from the way that data is understood and the importance of the way the algorithms are used. 

“There are so many data scientists now. It’s so easy to get a new model or a key, there’s so many libraries. So, building the model is not a big thing anymore. But for me, I think how you interpret the models, how you select the models, you have to have a kind of understanding. 

This differentiation between data scientists who build models, and data scientists who interpret them will grow in future years. 

“There will be a core role of a data scientist who actually decides how you interpret the data and chooses which algorithm we’re going to use. 

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