Why Data Scientists Are Increasingly Quitting Their Jobs: Lack of Skills or Different Expectations?

MAY 05, 2022

data scientist’s stock is currently at an all-time high. As we betwixt 2022, there aren’t many careers that can match the mystique, glamour, and respect that a data scientist commands.

I’ve seen non-data science (or non-technical) people regard a data scientist as having superpowers. There are a variety of reasons for this, including media hype, but there’s no denying that a data scientist’s job is highly regarded.

To support these claims, I have listed down a couple of reports on the most promising jobs, I’m certain you guessed the job at the top of the list:

  • Over 11.5 million data science jobs will be created by 2026, which is massive (The United States Bureau of Labor Statistics)
  • There is a 29 percent increase in demand for data scientists (Indeed, 2021)
  • The average salary of an entry-level data scientist is USD 93,167 per year (Glassdoor, 2022)
  • The demand for knowledgeable and skilled data scientists will boost by 2026 leading to a 27.9 percent rise in employment ( The United States Bureau of Labor Statistics)
  • Employer demand for data scientists will increase by more than 39 percent (IBM)

The aforementioned figures are jaw-dropping. From Fortune 500 businesses to Startups, organizations all across the world desire to establish a bunch of talented data science professionals. Without a doubt, the demand for data scientists is great, remunerations are competitive, and benefits are plenty, which is why LinkedIn has named data scientist as the most promising career.

Nonetheless, despite all of these encouraging tendencies, there is a nagging feeling of unease.

At an alarming rate, data scientists are resigning or changing jobs…

What is causing this? Is there something we’re missing out on?

Let’s skim through some of the major reasons why data scientists are leaving their ostensibly ideal positions.

4 Major Reasons Why Data Scientists Are Leaving Their Ostensibly Ideal Positions:

???? 1) Presumption vs. reality: there is a huge distinction!

This is among the most common problems in the world of data science. The distance between what data scientists assume and what they do in the company is expanding. A range of factors play a role in this, and they may differ from one data scientist to another. The gap between expectations is determined by one’s level of expertise as well.

Let’s use the case of zealous data scientists as an example. They are mostly self-taught and have acquired their skills from books and videos. They have limited experience with real-world applications and datasets. Personally, I’ve also met a lot of budding data scientists who had no notion about:

  • The significance of data cleaning, as well as the fact that it consumes a good deal of time
  • What is a machine learning pipeline and how does it work?
  • What does it mean to put a model into production/deploy a model?
  • The importance of software engineering in the total skill set of data scientists

????????The presumption

Freshers (and everyone else, to be upright!) are enticed by the opportunity to experiment with fancy machine learning techniques and cutting-edge frameworks.

The truth is that the industry does not operate in this manner. There are far too many variables at work for a data science project to resemble what we see in online data science events.

????????The reality

The company wants you to know how to process and store data, how to effectively handle version control, and how to put your models into production, to name a few crucial features. This misalignment of perceptions is a fundamental hurdle that causes data scientists to leave their positions.

?????The solution

To bridge the gap between anticipation and reality, I always recommend freshers and novice data scientists talk to their seniors and company alumni regularly.

???? 2) Data science professionals lack training and guidance

Who doesn’t enjoy taking on new challenges? Given the rate at which advances are made, I would suggest that the data science profession is ideal for these issues. Consider the Natural Language Processing (NLP) domain; the quantity of changes that have occurred in the last two years is incredible.

Mostly every data scientist would jump at the chance to work on these cutting-edge methods and frameworks. Who wants to spend years creating and iterating on a concordant logistic regression model? The role of data scientists is not immune to the element of stagnation. After a given amount of time, you’ll strike a brick wall, and the need for a new challenge will always be there.

Here are three significant reasons for employee turnovers that I’ve seen:

  • Inadequate research and development: As a data scientist, you want to learn more about the field and expand your horizons. For example, if you are a computer vision specialist interested in learning about NLP, an R&D zone would be the greatest venue for you. Most businesses lack this, which leads to attrition.
  • Infrastructure deficit: Most firms lack the infrastructure, such as computing systems, access to tools, and so on, to support the function of a data scientist.
  • Business purpose: The company’s operational capabilities may be limited and constrained. It may become impossible for a data scientist to derive more insights from data beyond a certain point.

???? 3) Data scientist’s role concerning business objectives

Here’s another difficulty with unpopular expectations. This is mostly due to the recent excitement around artificial intelligence and data science. Executives, CxOs, C-Suite executives, and investors all want to prove that their company or project is at the cutting edge of technological advancements.

????The issue

We’ve seen a lot of these senior executives assume AI is a panacea for their business woes. They’ll find an answer in double-quick time if they engage in AI and the proper expertise.

Unfortunately, that is not the case. Before arriving at a final result, data science initiatives usually include several tests, trial and error procedures, and repetitions of the same process. The process takes months or years to achieve the desired result.

Although AI and data warehouse infrastructure requires a large investment (depending on the size of the company), the work’s discoveries may take time because extracting useful insights from vast swathes of data can be time-consuming. This is why data scientists need a flexible strategy that allows them to work on data at their own pace and in their location.

This is not well received by corporate leaders in a variety of fields. As a data scientist, I’ve seen this result in a huge influx from projects when data scientists get dissatisfied with their senior leadership’s high expectations.

?The solution

Business leaders and data scientists need to:

  • Develop a quantitative performance matrix for the business to track the advancement of data scientists’ performance
  • Have agility as it is crucial in getting the most out of a data scientist
  • Use business leaders’ intuition and knowledge to your advantage. For data scientists, this can be quite beneficial
  • Ensure that the data science and business teams are communicating effectively. They must be coordinated and unified.

???? 4) Exposure to different platforms and different data-driven projects

Which of these two possibilities would you most like to have:

  • ???? A very flexible work life in which you can work remotely and clinch significant self-growth?
  • ???????? A 9–5 job in which you must align your talents and outcomes with the company’s goals

The first choice is likely to be chosen by the majority of you. Who doesn’t like work freedom and the ability to choose what you want to focus on?

A data scientist today has a myriad of options to pick from such as freelancing (data scientists are familiar with Spark, SQL, Neo4J, Hadoop, Hive, Pig, MySQL, Python, R, Scala, TensorFlow, NLP, and anything else related to machine learning) personal branding and so on.

For obvious logistical and project-related reasons, most of these cannot be offered to resident data science professionals. To be honest, this is an unavoidable cost of any endeavor.

How can organizations retain their proponent data scientists?

I strongly believe in the tried and tested methods mentioned below, which can be applied by businesses to retain their data scientists.

???? Build a powerful learning environment

This is critical for an individual’s personal and professional development. This profession is exploding with new opportunities every day.

Talking about the elephant in the room, it’s vital to provide a progressive learning environment for data scientists. Employers must provide opportunities for training and skill development through a range of methods, including on-the-job training, data science certifications, and so on. Some of the institutions that offer these certifications are:

???? Create a powerful research and development group

Putting together an R&D team can help you do high-quality research in the field. Allowing staff to perform in-depth research is a formula for success.

The last word…

Considering the immense competition and the need for top-class skills data scientists must spend some time learning the ins and outs of the field they are working in. This knowledge can be used to ask the correct questions to the management and ensure that their expectations are in line with the project’s probable outcome.