What Is Causing Data Scientists To Quit Their Jobs?

You spent years studying data science in the hopes of breaking into the field as a data scientist. After several rejections, you finally receive a response from an interviewer. You are going to get hired. All of your efforts have finally paid off.

While this appears to be a very pleasant conclusion, the story does not finish here. You are demotivated, fatigued, and tired after a few months on the job. The company expectations are quite different from your outputs and your employer is always breathing down your neck. The models you create are just not resulting in sales.

You finally decide you can't take it any longer. You begin looking for a new job and submit your letter of resignation.

This situation is spine-chilling!

Unfortunately, in the data industry, this is a relatively regular occurrence. The issue is that the majority of data scientists are unable to derive economic value from the models they create. And, to be honest, this isn't always your fault. Let's look at some of the major reasons why data science professionals quit their jobs.

The conflict between reality and expectation

Expectations and realities are vastly different. Many junior scientists agree to professional commitments without understanding the realities of their work. Erroneous expectations arising from real-world work situations, and identifying them all would be impossible. Many ambitious data scientists believe they would be expected to create out-of-this-world machine learning algorithms or solve hard problems effectively to make life-changing judgments.

Aspiring professionals are sometimes self-taught via data science specific books, online courses, without earning best data scientist certifications, which provide sufficient knowledge until the individual is exposed to real-world datasets.

There are a lot of new data science professionals that aren't well-versed in fundamentals like:

  • Functionalities of a machine learning pipeline
  • Implementation of a model
  • Importance of data cleaning

All of the aforementioned fundamentals can be learnt by earning data science certifications.

Uninformed expectations

Unrealistic expectations about data scientists' abilities are not restricted to non-technical firm management. Almost everyone who works with them assumes they know everything there is to know about data and machine learning. It frequently outperforms other professional fields such as computer programming, statistics, and so on, and you must know everything there is to know about them. Assumptions lead to more assumptions; if you know something, you have access to its data, and if you have access, you have all of the answers to all of the world's issues.

There is a way to counter this issue!

The simplest approach to avoid this issue is to avoid jobs with descriptions like "We are seeking for a data scientist that is familiar with R, Scala, Hive, Pig, SQL, anything machine learning and data related." It clearly screams of a corporation that has no understanding what data science professionals can do or how to use data. They will expect anyone with a résume that includes data to solve all of their company's data problems.

Lack of flowing

Growth is hampered by stagnation. You cannot possibly expect to have a fixed skill set in a world where changes happen at breakneck speed. Data professionals in particular, thrive on new problems, which is fortunate because data science is one of the most challenging fields today. The domain of Natural Language Processing (NLP) is the best example of data professionals' quick progress.

Motivation issues need not always be restricted to freshers’ or junior data professionals; it happens to senior data science experts as well.  Bureaucratic environment at work leads data professionals to quit their high-profile jobs.

What can you do differently?

  • Earn best data scientist certifications to learn new skills and stay updated.
  • Avoid working in companies without a dedicated data science team
  • Ensure that you understand the context behind building any model
  • Understand trends between the existing data and projects developed in the past, learn marketing strategies to better target your potential and present customers
  • Be transparent about the data in-hand. Let your employer know that the data is not sufficient to provide tips or recommendations to drive growth.
  • Make sure you are a part of every step of the process. Do not be hesitant to inquire. Make sure you know how your model affects sales and that you can see the results of your analysis.  


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