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Keeping Up With Data Science
Tech experts Yong Kai Chin and Jason Chen Jiasheng from Sense Infosys share details of their life on the job and the skills needed to succeed in Data Science, a career field that has become the talk of the town.
If you look up the hottest career fields in the IT and tech sector today, chances are you will find Data Science on the list.
The Harvard Business Review went as far as to call data scientist “the sexiest job of the 21st century”.
Businesses from across sectors hire tech wizards from Data Science companies to work their magic and uncover crucial data that can help them with insights to improve their operations. They can identify and determine patterns from massive bulks of data to predict emerging trends for better performance.
Data Science has undoubtedly gained traction as a career field, but amidst the attention lies several questions – What is it like to be a Data Science professional? What does it take to become successful in this field?
To get an insider’s perspective, we speak to Co-Founder and Chief Technology Officer (CTO) Yong Kai Chin, as well as Technical Manager and Data Engineer Jason Chen Jiasheng about their roles and experience in Sense Infosys, a RiskTech company that builds intelligence products and solutions for clients.
Life on the job
As Technical Manager, Chen provides technical directions for projects and to product delivery teams. However, this is only one facet of Chen’s role. The other side of his job, which is essentially his role as data engineer, is more hands-on.
“I build, test, maintain and improvise the infrastructure of data processing systems. These involve extract, transform and load, as well as other processes to support data scientists or analysts in acquiring and using data,” Chen said.
Despite being a data engineer and having a background in engineering instead of computer science or mathematics, Chen explained that he plays an essential role in “the ‘science’ of Data Science”. He added that data engineers are required to work closely with data scientists to ensure that the existing architecture supports their work.
Typically, data scientists apply their theoretical knowledge in statistics and algorithms to identify trends and variables that make up a pattern. They are then able to come up with predictive models to help solve business problems faced by clients.
Data engineers, on the other hand, commonly come from a software engineering background and they provide the architecture for data scientists to carry out their duties by using their programming and coding abilities to source, store and process data. They are also required to implement algorithms and models determined by data scientists in code.
As such, both roles complement one another, and individuals in those roles need to work together as a team to effectively and efficiently solve data problems.
As Co-Founder and CTO of Sense Infosys, Yong understands this as he works with his team on a day-to-day basis to conduct reviews on system designs, system quality, UI/UX and security designs to make sure that all systems created are interoperable and reusable. He also has to lead his team to steer the technological direction of the company towards reaching its business objectives.
Before assuming his role at Sense Infosys, Yong worked at DSO National Laboratories as Principal Member of Technical Staff (PMTS) – but he left the job because he wanted to do something different.
“I have always wanted to build technologies that impact people’s work and lives positively. After spending nearly nine amazing years at DSO National Laboratories, I now lead my team at Sense Infosys to develop cutting-edge products and solutions that make the world a safer place for everyone,” Yong said.
Skills to succeed
For Yong’s wish to create a better place for the public to come true, professionals of different skill sets need to band together to contribute and solve problems as a team. According to Yong, collaboration between professionals of different expertise is crucial in the IT and tech industry, and he also championed the importance of soft skills, such as analytical and communication skills, in the process of successfully coming up with the right solution for the right problem.
“The ability to work with team members as a team is important. A solution does not only require data science know-how, but involves other expertise such as software engineering, effective user experience design and intuitive app design,” Yong said.
“What engineers and data scientists try to do, at the heart of it, is to understand and solve a business problem. Soft skills are definitely necessary for us to be able to talk to people, understand issues and identify problems,” he added.
While soft skills are essential for a successful career in Data Science, one should also have the technical skills to do what is required of the job to begin with. Aspiring data scientists should be skilled at mathematics, programming and statistics, and they should also be well-versed in statistical analysis and have knowledge of computing frameworks to mine and process raw or unstructured data.
“Big data architecture skills, such as Hadoop are high in demand. Also, timeless skills such as algorithms and programming are important as well, alongside knowledge on how to collect and store data,” Chen said.
Coping with the changing landscape
Having the right technical and soft skills may land you a job in the Data Science industry, but according to both Yong and Chen, the key to a successful long-term career in the IT and tech industry is curiosity.
“It’s a fast-moving and ever-changing industry, and you need to have a curious mind to keep up,” Chen said.
“Curiosity is an important quality to have to be successful in this industry, specifically the curiosity to understand and appreciate the constituents of a problem, before coming up with a Data Science solution,” Yong said.
The CTO also said that he makes a consistent effort to keep abreast of emerging technologies. He does this by reading widely and regularly, such as technology news and research reports, while seeking opportunities to build products and solutions with the knowledge of these new technologies. Chen resonated similar sentiments.
“You should learn and read about the ongoing changes in technology. You need to update all that you learned in school because it is not enough to keep up,” Chen said.
The tech world is undeniably fast-paced, and it is difficult to predict what the next big trend will be, or how long Data Science will remain relevant in the years to come. When asked about the future of Data Science, Yong responded by saying that Data Science will become commonplace in the next few years. He explained that even in today’s landscape, “Data Science is no longer a nice-to-have but a must-have capability for businesses”.