The challenges of being a data scientist in a corporate world

Data Science   |   
Published April 13, 2018   |   

It’s been a couple of years since the world was introduced to an exciting new career prospect. But the hype around data science still hasn’t died down. With it being predicted to remain the ‘hottest’ job for the next couple years, it doesn’t look like the buzz around data science would go away anytime soon.
And why not? Data science and its related job profiles have increasingly been the turning point of many enterprises. Quite a few companies have been hopping onto the data science bandwagon in the hopes of better revenues and fresher business solutions.
However, as promising as it is, there are several challenges and difficulties which a data scientist has faced in their career.

More than what meets the eye

Despite being one of the highest paying jobs in the software industry, everything is not all hunky dory in the world of data science. Studies show that nearly 13.2 percent of data scientists are looking for new jobs. As reported in an article published by the Financial Times last year, the field of data science has the second highest number of people who are unsatisfied with their jobs.
But why are data scientists so unhappy with their jobs?
At first, one tends to blame the candidates themselves. Due to the increased intertest, the demand for well qualified data scientists in industries is high. And so, individuals looking for jobs within the data science spectrum sure do have their fair pick of the crop. Which would lead them to be fickle and picky with the opportunities they explore.
However, the blame does not fall entirely on the candidate’s shoulders. A large portion of it falls on the companies who are looking to incorporate data science in their organizations.

Is the corporate world ready for data scientists?

With the interest around data science increasing, it is not surprising that every company wants to be a part of it. Data science is the cool new toy of the corporate world. And every kid on the block wants one.
However, being a relatively new field, not many companies know what to expect while setting up a data science department. Blindly following suit because everyone else is doing it, companies hire data science candidates without fully comprehending the necessity or the purpose behind it.
As Q McCallum mentions in his blog post, there needs to be a certain amount of preparation before a company should start hiring data scientists. Which includes compiling and preparing the data that must be analyzed. One of the major reasons why data scientists tend to a leave a company is the sheer amount of data that they are expected to sort through before they can begin the work their job profile calls for.
They end up dealing with poor quality of data, which include incomplete values, missing samples and poor representation of the samples they do have. This leaves them feeling discontented because their full potential as a data scientist is not realized.
On top of that, companies directly hire junior level data scientists with little to no experience in the field, since they do not expect much in terms of salary. But, without a senior to guide them, these rookies are left to navigate the large amount of dirty data on their own. This usually leads them to feel lost and frustrated. Eventually, they leave the company for more satisfying job opportunities.

The dilemma of a data scientist

Another major reason that drives data scientist to quit or change jobs is corporate politics.
Okay, admittedly, any politics in an organization can make anyone’s job a lot more difficult than necessary. However, since data science is supposed to have a direct impact on the improvement of revenue of the company, data scientists are often caught in the cross-fires of the upper management. So, it becomes extremely important for them to be on the right side of the right people.
Which means taking on a lot of additional tasks that have no relation to their job description. They become the go to person for anything related to data and numbers. And are expected to have the answers to everything at the right time.
For instance, data scientists are expected to translate the data into relevant points of action. Because, in all honesty, upper management is not interested in the numbers, but are interested how these numbers can be used to generate better revenue for the company.
And in enterprises which never had data scientists before, there would be certain amount skepticism from parts of the management. So, the data scientist must answer questions of individuals who do not buy in to their analysis and forecasts. Not to mention they are sent on a wild goose chase trying to sort and compile all the raw data in the first place. Which leads to resistance in data collection from the skeptics.
All of this put together can put a significant amount of stress on a data scientist.

In conclusion

In retrospect, the ultimate reason why individuals lose interest in their data science jobs is because the job never really lives up to their expectations.
When junior level data scientists first enter the field, they have a glorified image in their heads. They believe that they would be to solving complex problems using cool algorithms, and overall having a significant influence on business. And considering all the hype which surrounds the job description, it is not surprising that things tend to get a tad exaggerated.
However, after having mentioned some the challenges which data scientists face, it in no way means that aspiring data science candidates should be discouraged from pursuing a career in it. Borrowing the words of Jonny Brooks-Bartlett, a data scientist himself, the job can be fun, stimulating and rewarding.
If you think about it, every job available has their own set of challenges to overcome. What’s important is to find a place where you can fit in and enjoy what you do.