We have always heard about data science being the “sexiest job of the 21st century” and the technology enthusiast. And young graduates and also, working professionals have already started to take data science as their career option. How many are aware of the brutal truths about data science career?
Nothing in this world comes without cons and data science is no exception. In this article, we are going to see some of the most brutal truths about the job role of a data science professional.
1. The JD And The KRA Vary Greatly
There are many companies across the world to leverage data. However, not all the companies have a strong and dedicated Data Science department or environment for the professionals to work.
When you get started with your Data Science career, you build some sort of expectations and when you actually join a firm and start working, the reality is different. Many organisations put up ads about data scientist hiring and mention specific roles and responsibilities, making a lot of candidates to apply.
Data Science aspirants usually expect a job role where they can use their skills to solve some of the complex problems, however, that is not what they get to do in many companies as they are assigned with works that have less touch of data science.
2. Many Companies Are Still Hiring Data Scientists Who Are Settling Down For A Low Paycheck
You have 10+ years of experience in the core data science domain. You can solve the most complex problems, can code from scratch, you can even build some of the most efficient machine learning models, but you are still not getting hired. Why? Because many companies cannot afford you and the companies who can, they already have the slot filled.
Data Science is an expensive field of work, the professionals spend a significant amount of money to learn and gain knowledge and in this competitive era, experience data scientists are not ready to settle for less.
When companies are not hiring significantly experience professionals that don’t mean they are compromising, they just don’t have the budget. So, instead of hiring someone with 10+ years of experience and pay him/her significantly high, they would hire someone with comparatively less experience and train him for the works the company wants to get done.
3. You End Up Utilising Your Personal Time For Learning
Data Science is a very vast domain — the learning process never stops. You might be trained in a particular vertical in data science domain, but when you join a company’s data science department you might have to learn few more things, which you can during your work hours. To be an efficient data scientist in the company, you might have to spend extra hours for the learning process — either through self-learning or online course or with the help from your fellow data scientists.
4. Experience Speaks Louder Than A Degree
Here in this point, we are not saying that a degree or qualification is of no use, rather we are saying that there will be a point when you switch companies that your experience will make a huge difference compared to what degree you have.
You might have a high-level qualification from the field of data science, but if you don’t have the know-how some of the latest trends in the industry and lack real-time experience, it would get hard for you to get hired. Today, companies want people who can deliver the clearest and meaningful insight out of cluttered data, not someone who has only theoretical knowledge. This is one of the bitter truths.
5. Takes Time To Get Noticed In The Industry
Over the past couple of years, the data science domain has reached such a level that the number of people taking data science as a career is increasing. As we are being brutally honest, here is one more — it would take you a lot of time to stand out in the industry. Data science enthusiasts across the world are working day in and out on projects and expanding their portfolio. So, you just cannot expect to be the best in the industry in no time until and unless you are exceptionally brilliant with what you do.