Data Scientist

Help organisations make better decisions by collecting, analysing, and interpreting data.
Ivy Simon
Editorial Writer
Help organisations make better decisions by collecting, analysing, and interpreting data.

As we use technology in every aspect of our lives, we log and generate vast amounts of data, which is captured by businesses, governments, and other organisations. However, all this Big Data is useless on its own unless someone analyses and interprets it in a way that organisations can act on. That is the role of data scientists.
Data scientists essentially help organisations manage and archive the Big Data they collect from users, while also analysing all that data for actionable insights that support organisational goals. This can range from helping businesses forecast consumer behaviour, find new revenue opportunities, and pre-empt fraudulent transactions, to helping government agencies plan resource allocation, forecast tax revenue, or identify potential national security threats. 

Though data scientists should possess a solid background in computer science, modelling, and statistics, that isn’t all that’s needed! Having a keen business sense and the ability to communicate findings is crucial too, given that this role involves advising others on how to resolve business challenges.

Career Overview

Data scientists may have different functions depending on what industry/sector they are involved in. For example, a data scientist working with an e-commerce platform might analyse the types of items users browse, and then use this information to decide what types of advertisements to target the user with in the future.

Organisations typically start graduates off as data analysts. Your role at this level will involve sorting through and finding specific trends and patterns concealed inside enormous amounts of data. You may also be asked to create data models that can assist with important business choices, or write algorithms to automate data-driven decisions.

An increasing number of employers now run graduate programmes specifically onboarding or retraining candidates for data science roles. The main programming languages typically used within analytics, data mining, and data science are R, SAS, Python, and SQL; though knowledge of Java and C/C++ is important as well.

As data science is still a relatively new area of work, career progression pathways can vary greatly. Graduates may advance higher up the ladder into broader information management roles, or move horizontally into more specialised data analysis projects. There is also the option to eventually branch out into consultancy roles, using data to advise businesses more generally.    

Trends and developments

With the proliferation of Big Data across all industries, data science is one of the most in-demand areas of work right now, especially since Malaysia is still facing a high shortage of data professionals. This demand is projected to increase further, given that ICT spending by Malaysian businesses is expected to reach US$25.2 billion by 2023.

Cloud-based data storage and analytics platforms continue to be a key growth segment for the data science industry worldwide. However, it’s worth noting that not all organisations want their data stored on the cloud. Organisations may prefer to store sensitive data either offline or “nearline” (semi-online) for security reasons, and its important for data scientists to know how to manage data on such platforms too. 
The work of data scientists is also becoming increasingly tied up with AI worldwide, as algorithms acting on data become more and more complex, the hunger for more Big Data to feed into machine learning models continues to grow, and the demand for more and more seamless automation of business processes increases. 

Pros and cons

Because data scientists are in demand across all industries, you have the opportunity to work across a variety of sectors and fields.

Technological gaps
Because Big Data is constantly growing and evolving, not every organisation is fully equipped to properly manage and access all the data it collects. Figuring out how to overcome these obstacles will be key to getting your work done.

Required skills

  • Analytical skills
  • Problem solving skills
  • Investigative skills
  • Keeping up to date with technological changes
  • Communication skills
  • Commercial awareness