Data Science is Losing its Value as a Top Tech Job. Here’s Why!

This is why data science professionals are worried about their jobs.

Remember all the articles and blogs that said data science is the hottest job trend? The case might not be that anymore. According to the clout, aspiring data scientists, who have not professionally joined the field yet, want to get into data science because it involves solving complex problems with fancy machine learning algorithms that add value to a business. But unfortunately, the expectation doesn't match the reality. 

While there are companies that do everything just right, many other companies hire data scientists without the foundation of the right infrastructure to start working with artificial intelligence. Adding this to the fact that these companies often hire freshers who can't work their way around it, instead of experienced professionals, causing internal disagreements. The data scientist might have joined the company with hopes to create advanced machine learning algorithms to get valuable insights for the business, but gets stuck while sorting out the data infrastructure and creating analytical reports. As a result of this, the company gets frustrated for not seeing quick results and that leads to total demotivation. 

Is there a solution to this conundrum? Yes. For junior data scientists, it is important to identify a company that aligns with the job expectation in mind. But with so many companies looking to hire data science professionals, that is easier said than done. 

Another expectation-not-met that is demotivating data scientists is the core objective of the said company. Data scientists have a desire to make an impact on people with their insights. But if the company's focus is not machine learning, chances are, the only job you will do is provide small insights that add to small gains. Those insights might add up to something significant, but your part will be minuscule. Having a thorough knowledge about machine learning algorithms is required, but that is not the ultimate quality that will make you the star data scientist in a company. To make a good reputation in the business world, one needs to do a lot of ad-hoc tasks, like crunching numbers from a database and communicating it to the right people at the right time, to get recognition. 

Will that make you a respected data professional? Not entirely. The same people you are trying to impress by doing ad-hoc tasks might not even understand what you do as a data scientist. This means you will be the go-to guy for analytics advice, a database expert, and a reporting person. There is a prevailing assumption that a data scientist knows everything data-related, like Spark, Hadoop, Hive, Pig, SQL, Python, NLP, and anything machine learning. With assumption comes the mindset that you, as a data scientist, will have all the answers which should be communicated in just 5 minutes. So, if you see a job description with every data-related word stuffed into a paragraph, understand that the company is clueless about its data strategy. 

So, what does it take to be a good data scientist in the industry? Apart from becoming a knowledge expert, you need to bear through the unexpected work which might seem menial, know how to go along with office politics, and deal with co-workers misunderstanding what you do. Finding a company that matches your ideology is difficult and time taking, but not impossible. Otherwise, these factors are proving to be deal-breakers for many data science professionals.