Who does what in the world of data science? How much could you earn in the industry? We take a look at what you can expect.
During Data Science Week on Siliconrepublic.com, we’ve looked at the differences between roles in the industry, how to get your foot in the door and the skills you’ll need to be successful. But what’s the current job market like?
Data science roles are in high demand. According to Analytics Insight, opportunities in the field continue to grow as businesses are “generating massive amounts of data” and need to extract useful insights from it. New possibilities are cropping up that require skilled professionals as tech continues to become more sophisticated.
So it’s perhaps no wonder that Harvard Business Review named data scientist as the “sexiest job of the 21st century”. But if you’re unsure whether or not it’s the right career path for you, learning from the experiences of others could help you decide.
Will a career in data science meet your expectations?
When we think about data scientists, the image that might spring to mind is someone carrying out solo work on a computer. But working in the field has given Tal Segalov of Anaplan a different outlook.
Not only should data scientists have a penchant for learning, he told Siliconrepublic.com last year, they should also be able to communicate effectively and listen actively. Working with data often means you are working with stakeholders in a range of areas such as business, marketing and sales, so it’s essential that data scientists “learn from the experts” to get domain knowledge in those areas.
“We also have to listen and communicate with our customers and partners to understand their specific needs and unlock the best way to address them,” Segalov added. “Being a data scientist doesn’t mean sitting alone with your computer all day. To create effective products, data scientists have to talk with field experts, customers and partners to make strong products that are impactful and can be used across the entire organisation, not just within the world of data science.”
What are the different roles?
The main duties of a typical data scientist involve organising data. They draw on languages such as R, SAS, Python and Matlab to carry out distributed computing, predictive modelling and storytelling.
Data architects take the data and create blueprints for management systems. Ultimately, this means the data sources can be integrated, centralised and protected. They require skills such as Hive, Pig and Spark and an in-depth knowledge of database architecture, data modelling and ETL and BI tools.
For data engineers, knowledge of SPSS, Java, Ruby and Perl, data APIs, database systems and data-modelling tools help them develop, construct, test and maintain the architectures.
How much could you earn?
As of October, Glassdoor says the average yearly salary of a data scientist in Ireland is just under €50,000. Data analysts make just over €35,000 on average and senior data scientists can earn almost €73,000 per year. The average salary is just over €58,000 for data engineers.
Your salary may also depend on your knowledge of different languages and tools. Freelancing platform Upwork recently revealed the highest-paying programming languages across its site, with Objective-C, Golang, Windows PowerShell, Excel VBA and Kotlin among the top candidates.
Can you move into data science from another field?
Data scientists need a specific set of technical skills to carry out their job. But softer skills are also crucial and pivoting from one industry into data is not only possible, it can actually give you an advantage.
NuData Security’s John Hearty, for example, originally studied philosophy. Without any prior knowledge of coding, he completed a master’s degree in computer science and quickly became “hooked”.
The skills he had cultivated during his undergraduate arts degree served him well in the world of data science. In particular, he said that conditional decision-making is an important aspect of data science that requires reasoning, the ability to create something useful in complex circumstances and the capacity to identify “what really matters” when developing solutions.
Other soft skills you’re likely to learn in other industries that can grant you an edge in the world of data include communication and the ability to think differently. As applied data scientist Vin Vashishta previously told us: “A complete answer to ‘what is a data scientist?’ can’t leave out soft skills.
“The hard skills such as programming, math and stats get most of the visibility because there’s so much potential business value there. The soft skills are what turn that potential into reality.”
What’s the industry like for graduates?
What can you expect from entry-level roles if you choose a career in data science? For physics graduate Cristina Prieto Angulo, her time at EY began with shadowing a more senior member of staff and learning the basics of coding languages she hadn’t engaged with before. Now, she’s growing her skills through a project automating consolidated reporting processes for one of the company’s clients.
For Laura Sinnott, an internship at Aon’s Centre for Innovation and Analytics (ACIA) during her statistics degree solidified her decision to become a data scientist. She now works full-time at ACIA and since starting, has learned valuable lessons about the field.
It was daunting joining ACIA as an entry-level data scientist, she recently told Siliconrepublic.com. “While university prepared me for theoretical practices in statistics and analytics, it couldn’t prepare me for the fast-paced insurance industry and the highly technical platform of tools used in the centre.
“Looking back, I felt overwhelmed at first, but I soon found out that there is nothing quite like learning through hands-on experience. Having the opportunity to learn from industry experts while leveraging cutting-edge technology was invaluable in my development.”