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Data Scientist: What makes it the 21st century’s sexiest job?

People often wonder how Netflix’s personalized recommendation system works and how it knows what their preferences and viewing patterns so well. The secret here lies in the role of a Data Scientist, also known as the most sought-after job at the moment. So, what makes it the 21st Century’s sexiest job, and what skills should university graduates and professionals possess to land the job and secure job protection?

What is Data Science? What does a Data Scientist do? 

Data Science is a multidisciplinary blend of scientific methods, processes, algorithms, and technology that extracts insights from a data source, which will then be utilized by companies to make better business decisions. It requires techniques and theories drawn from various contexts such as mathematics, statistics, computer science, and business or economics to collect, clean, and understand biases in data. Once getting data into shape, the Data Scientist will conduct data analysis, which is a combination of data visualization and data sense to identify patterns or trends, discover solutions or opportunities. So to answer the previous: Netflix’s data scientists uncover meaning from and interpret movie-watching patterns to understand complex behaviors and interests, and cater relevant recommendations to viewers.

What makes it the sexiest job of the 21st century?

Coined “the sexiest job of the 21st Century” by the Harvard Business Review in 2012, “Data Scientist” shows the demand will not be slowing down at least in the near future. As businesses rely heavily on data to help them become much more intelligent enterprises, the role of the Data Scientist in altering a company’s destiny has become ever more sought-after. Moreover, it also ranked #6 in top high-paying jobs in the U.S., with salaries ranging from $116,000 to $163,000 in 2018.

Rank 2018 2019
1 Engagement Lead Data Scientist
2 Software Engineering Manager Site Reliability Engineer
3 Customer Success Manager Enterprise Account Executive
4 Solutions Architect Product Designer
5 Sales Director Product Owner
6 Engineering Manager Customer Success Manager
7 Program Manager Engagement Manager
8 Product Manager Solutions Architect
9 Data Scientist Information Technology Lead
10 Enterprise Account Manager Scrum Master
Changes in the Top 10 most in-demand jobs from 2018 to 2019. (Source: Upskill Create)

So why is Data Scientist in such high demand? There are many reasons contributing to this, however, the 2 main factors and trends that lead the job position to grow dramatically in just a few years are associated with (1) a data-driven culture and (2) a shortage of expertise and talent.

   1. Data-driven culture

No longer restricted to technology and financial services, organizations across almost all industries nowadays depend on insights and intelligence extracted from data to benefit the decision-making process and tackle the most business-critical and complex challenges, allowing their businesses to make revolutionary leaps forward. For instance, while consumers in e-commerce can benefit from customized offers and personalized products, healthcare can identify chronic disease trends, offer patient predictive analysis, and provide personalized treatments. Back in 2012, data science has proved to bring significant advantages to enterprises. Companies in the use of data-driven decision-making were, on average, “five percent more productive and six percent more profitable than their competitors.” This brought greater economic potential and was reportedly an increase in stock market valuations. 

   2. Shortage of expertise and talent

The demand for Data Scientist does not seem to be disappearing anytime soon, so why the supply shortage? The August 2018 LinkedIn Workforce Report found that there were more than 151,000 Data Scientist job positions going unfilled across the U.S. 

Trying to make sense of data then use it to solve business problems, spot trends and make decisions to support new ideas requires professionals possessing multiple skillsets, from statistics and data visualization to machine learning and software programming, which must be matched with a strong sense of business intuition. An unqualified candidate might have some domain knowledge, but is probably lacking the experience and other specializations needed to be considered for most jobs. Moreover, the lack of soft skills such as effective communication and collaboration skills to work with interdisciplinary teams and clients is also a key challenge. Altogether, this sophisticated mixture of skills is the reason why the job suffers from a “massive talent gap”, making it the rarity despite having existed for more than a decade.

Shouldn’t those skills be trained at universities, colleges, or the like? The answer is, in fact, not so much. Most professionals and senior Data Scientists usually transition from other fields and gain the necessary skills mainly through self-preparation or on-the-job training provided by their companies, as traditional education was not ready to back then. Although educational systems around the world are starting to offer proper courses to prepare prospective Data Scientists for their career, it will take them some time to master those skills and have sufficient qualifications to fulfill the needs of organizations. 

How to meet the shortage of professionals in the field?

While the role of a Data Scientist might differ across different companies or industries, most companies require their professionals to possess a well-balanced mix of hard skills and soft skills, which many university and higher-education programs fail to offer.

   1. Critical thinking

Perhaps the most desired skill in this profession, a great Data Scientist must be someone who not only interprets but also interrogates data, which means they should have the ability to develop the question and question the data. Firstly, the process of developing the question requires collaboration between the data scientists and users, as well as the business owners who know much more about business goals and metrics. This will result in the effective sharing of knowledge and collaborative problem-solving. Further, one should always stay curious, be skeptical of the data, never look at things at surface level and jump to conclusions right away. Digging deeper into data will allow Data Scientists to find much more valuable insights that will be beneficial for both the customers and the entire organizations.

   2. Programming and technical knowledge

Professionals must be proficient in statistical programming languages such as R, SAS, and Python and database query languages like SQL. Software engineer and data manipulation skills, and the ability to develop complex data architectural frameworks, combined with knowledge in big data, artificial intelligence, machine learning, and deep learning will also help them handle data-logging, develop data-driven products, aid business decision-making processes and change the trajectory in the long run.

   3. Soft skills

Often overlooked, soft skills are also of great importance in data science. These include managing projects and teams across different departments, consulting with clients, articulate findings to business people, assisting with business decision-making and development, and turning abstract business issues into analytical solutions.


As technology evolves and the need to outperform rivals increases, enterprises are becoming more and more dependant on data to better understand and serve their customers. As such, this has led to the exponential growth of Data Scientists around the world, making it one of the hottest, most sought-after jobs of the century. The demand is also increasing due to the low supply of labor, as there are still not enough people meeting and the specialized requirements and exploiting the opportunities of the industry. In order to be qualified for the field, prospective Data Scientists must demonstrate particular skills that will then enhance business strategies and increase revenue for their company.

According to Woz U + Castille


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