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Data scientist: Yes, you can get ‘the sexiest job of the 21st century’

It’s been a decade since D.J. Patil, LinkedIn’s former head of data products, coined the term “data science.” And already there are thousands of data scientists working in different companies. This is a lucrative position, perfect for professionals capable of organizing large quantities of data for analysis. Data science seems to be the position of choice for analysts and managers, especially those who wish to take their careers to the next level. No wonder Harvard Business Review named data scientist “the sexiest job of the 21st century."

Forbes Magazine noted there are 2,900 individual job vacancies, on average, for the position of data scientist in 2016. And that number is expected to grow to nearly 200,000 open positions by the end of this year, as per a study conducted by McKinsey Global Institute. Unfortunately, no amount of endless opportunities in this amazing field can change the fact that transitioning from a non-science background to data science is hard. After all, there are individuals who’ve put in a lot of time and effort into perfecting their skills in a specific career path; for them, switching to data science can be challenging. However, if someone is ready to take the plunge, it can be done. But there are a couple of things you should know beforehand:

Why do you want to be a data scientist


First, understand your motivations for wanting to switch to data science. Switching careers can be a bit jarring. But never let the “newness” of the job detract you from achieving your target. Think about why data science is a good career choice for you. Consider the long- and short-term goals you see yourself fulfilling by switching jobs. You should proceed with the transition only if you’re comfortable with the new profession and everything it entails. This holds true for any new role, not just the field of data science.

Short-term goals

Data scientists are often hard-pressed for time and do not have the luxury of learning new technical talents. Together with their teammates, they focus instead on higher-level targets. So, if you plan on increasing your knowledge, you better be prepared to put in the commitment and time. It’s also a good idea to let others mentor and motivate you in the initial years. A mentor is someone who is a part of the data science industry and is capable of providing feedback regarding the areas that require improvement. Most importantly, they serve as your reference and inside contact when required. They can inform you about amazing job positions in the field that have not been posted openly but are circulated among networks of contacts. Having somebody watch over you and supporting your transition can make a huge difference to your career.

Long-term goals

As you climb higher up the data science ladder, you might get the opportunity to take on a leadership position with a less technical role. So, you need to work on your project planning and team management skills. Also, the organizations you choose to work with and the connections you forge along the way are going to come in handy. If you play your cards right, you will have no trouble realizing your goals and attaining a leadership position.

Cultivating the correct skills

Usually, the term “data scientist” conjures up images of people who have a knack for calculating numbers and handling large volumes of data. While this is somewhat true, a data scientist is also a serious academician who improves the organization through informed decisions. So, if you want to join the ranks of the best data scientists, you can start by figuring out which hard skills you lack. It could be Hadoop, machine learning, and statistics and analysis. You also need to have excellent communication skills, critical thinking abilities, and a problem-solving mind. You will find lots of resources online to help you with all this. Data science boot camps and online courses are also available. They serve as a crash course for improving your skills within a short amount of time.

What’s important, however, is the fact that if you wish to be taken seriously as a data scientist, you must never stop developing new skills. Keep an eye out for new opportunities and challenges. When appearing for an interview for the post of data scientist, mention how you are always learning new things and improving your skill set. This will reflect positively on your character and show the company that you’ve got what it takes to fit into a constantly shifting role.

Start using data science now

If you hail from a business background, you will be pleased to know there is high demand for managers who possess a strong data background. Such professionals are considered highly valuable, almost as much as pure data scientists. Thus, you will find numerous options for business-minded professionals who wish to adopt the new role of a data scientist.

The majority of data-related projects worked upon by organizations are not a one-person job. Their success hinges upon multidisciplinary teams working together in unison. Moreover, most companies wish to stay ahead of their competition and now rely on data to make important decisions. So, leaders and managers have to integrate as much data analysis skills as possible.

Anybody who wishes to transition completely to the role of data scientist must incorporate some data science skills into their present role. This will allow them to slip comfortably into the new position. For starters, you can add data-driven decision-making to your present tasks. If you’re serious about becoming a part of the data scientist community, you should also meet with others who share the same career goals by attending data-focused gatherings or getting technical mentorship.

You need to keep all these things in mind and adopt a learning mentality if you wish to establish your career in the field of data science. You might even become a mentor yourself one day, imparting knowledge to others as you learn yourself, thereby completing the cycle of data science. Your curiosity should pull you toward hard problems, and you must solve them to gain fresh perspectives from old datasets.

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