Unsurprisingly, data scientists took the top spot for the second year in a row in Glassdoor’s study identifying the 50 most remarkable careers. This report is publishing yearly by the employment website and is based on the “Glassdoor Job Score” for each position. The median annual base wage, the number of job opportunities, and the job satisfaction rating all significantly determine the score.
Data scientist jobs came in first with a job score of 4.8 out of 5, a job satisfaction score of 4.4 out of 5, and a median base income of RM21 000/year. Next came various technological careers, including data engineers and DevOps engineers.
Roles related to data have dominated similar job reports published in the past year. Jobs for data scientists, one of the most challenging jobs to fill. They have the most growth potential over the next seven years, according to a new analysis by CareerCast.com. According to data from rjmetrics.com, there were between 11,400 and 19,400 data scientists working in 2015. More than half of those positions had already been filled.
On LinkedIn, a fast search for “data scientist jobs in Malaysia” turns up over 5,700 opportunities available. Furthermore, the Indeed job trends tool that highlights the need for data scientists finds that neither job listings for data scientists nor interest from job seekers show any indications of slowing down.
According to the Computer Science Zone, there will be one million more computer jobs in the next ten years than there are workers to fill them. So how did the data scientist position become the most important one? Let’s look at some of the factors and patterns that caused the data scientist position to again rank as the top job this year.
Reason #1: There is a talent gap
Additionally to being in great demand, people with expertise in statistics and analytics are also those with complementary soft skills. Business executives are looking for experts who can successfully articulate their results while comprehending the numbers. Data scientists are expecting to earn more than $6% this year. This is because of the continuing absence of personnel who can combine these two skill sets.
To fill these positions, where are all the data scientists? The typical response to this query is that they have not yet received training. Even if the number of computer science programmes is growing, it will still take time for the supply to catch up with the demand. The lack of data science skills won’t be solving overnight. This is because big data and analytics courses have recently begun appearing in school curricula. Over the next few years, there will undoubtedly be more employment openings than there are individuals with a deep understanding of data and analysis to fill those openings.
Reason #2: Organising data continues to be extremely difficult for organisations.
Organisations need individuals who can handle data organising and preparation for analysis as the job of the data scientist changes. Data wrangling, or preparing data for use by integrating tools and cleaning it, is still in great demand.
Although several phases are involving in data preparation, from converting specific system codes into usable data to addressing inaccurate or missing data, the consequences of inaccurate data are significant. According to some research, evaluating flawed data can cost an average firm more than $13 million annually.
As a result, there will always be a need for people who can eliminate erroneous data that could change the outcome or produce inaccurate insights for a company. There’s no denying that it takes a lot of time. Data scientists spend around 80% of their time preparing data. There will always be a need for experts with the advanced skill sets required to clean and organise data before extracting significant insights from it, even with the increased accessibility of highly sophisticated analytics dashboards and data collecting platforms.
Reason #3: Data scientists are no longer only needed by IT giants.
Smaller businesses realise that they can also use data to make better, more educated decisions. This has increased the demand for data scientists outside giant technology companies like Google and Facebook. According to a big data HBR story, businesses that use data-driven decision-making at a higher rate than their rivals are typically 5% more productive and 6% more lucrative.
Sifting through that data to derive valuable insights into their businesses can still be a solid competitive advantage, even though small-to-medium-sized companies are not producing as much information as larger businesses.
Due to the belief that they could take on more challenging work sooner in their careers, entry-level data scientists also gravitate towards startups and smaller businesses. Because they have such a wide range of talents, data scientists want to be able to apply them all right away.
Smaller companies are also hiring quickly. To attract the top talent they want, large firms trying to employ entry-level data scientists realise that their multi-step, antiquated hiring and recruitment procedures may need to be updated. Therefore, agile firms continue to be the better option for data scientists. Regardless of their size, as the need for data specialists continues to soar.
Final Thoughts
All of the stargaze created by the corporations are built on data. It evaluates consumer behaviour and the attitude they are developing about a specific commodity or service. It becomes crucial for businesses to spend money on data analysts. This will enable them to deliver services that customers want. Technologies like data science are essential in this situation. Consequently, we observe an increase in the need for data science in Malaysia.
This article is posted on Article Rod.
