Why Do So Many Data Scientists Quit Their Jobs, we are aware of numerous data scientists who quit their employment shortly after being hired.

One week after starting, many of them left their data science internships because they believed the duties they were given had nothing to do with the skills they had meticulously learned.

We’ve learned through talking to colleagues in the data sector who left their positions relatively early in their careers that there are two key causes for the high employee churn rate in the data science field.

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Reason 1: Mismatch in Employer Expectations

You devote many hours to mastering statistics and the subtleties of various machine learning techniques. Then, after submitting numerous applications and going through a protracted interview process, you get employed by a mid-sized company.

You are eager to put all the knowledge you have gained over the years to use by working on actual machine learning problems.

On the other hand, the organization has a significant volume of unstructured data entering the system that hasn’t been prepared or processed in any way, as you discover on your first day of work.

Your employer views you as the go-to “data guy” and assigns you the responsibility of assisting him in increasing sales using the vast amounts of data they are gathering every day.

In the end, you’re not creating the sophisticated algorithms and predictive models that you had envisioned.

You now devote all of your time to brushing up on your SQL and data preparation abilities in order to extract data from the system, format it for diverse audiences, and deliver it to stakeholders for use in business decisions.

Despite the fact that “data science” is a part of your job title, you are not in the position you had always imagined for yourself.

You don’t like managing the company’s data, and you want to focus on tasks that make use of the abilities you’ve worked so hard to develop.

There are ultimately just two options left: either stay in the company for a few years and continue doing work you detest, or leave and find a company with initiatives that are more in line with your objectives.

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Here is the issue:

Although the aforementioned situation may seem improbable to you, it is one of the most frequent concerns we’ve heard from data scientists in our area.

Many of them came into the position with very different ideas of what it would entail, yet they frequently found themselves doing data reporting and analytical jobs every day.

Since they haven’t worked on practical ML projects in years after joining these types of firms, many data scientists wind up losing touch with their machine learning expertise.

They are unable to apply to mid-level or senior-level data science job advertisements when seeking employment since they lack the necessary experience.

These people frequently find themselves forced to change their job path, becoming reporting or data analysts.

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Reason 2: Inability to Add Business Value

The inability of machine learning models to deliver business value is another frequent cause of frustration among data scientists.

This problem, in our opinion, arises even more frequently than the preceding one since it affects firms with clearly defined job roles and effective data pipelines.

Here are a few reasons why data scientists don’t succeed in creating models that benefit organizations:

The enduring divide between technology and business:

The majority of stakeholders and senior management are non-technical, so they frequently are unaware of the potential of machine learning modeling.

Because of the buzz around the profession, your supervisors may make some rather audacious requirements of you as a data scientist.

It is your responsibility to explain to them whether a project can be completed effectively and whether it will actually produce the outcomes they are hoping for.

To avoid a lot of disappointment later, make sure expectations are in line with the likely outcome.

Before beginning any machine learning project, it might also be helpful to compile a cost-benefit analysis so that everyone in the organization can agree on whether it is worthwhile to devote time and resources.

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Not posing the proper queries:

Data scientists need to understand whether the models they are developing will benefit the company.

Based on the directions they are provided, the majority of data scientists I have encountered are quick to begin a project.

They don’t pose the proper inquiries. They don’t make an effort to comprehend their manager’s mental process.

When you merely do something because someone else tells you to, you lack awareness of the value you can contribute. You won’t be able to respond if asked to justify the value of your job.

If you don’t know why you’re producing your product in the first place, how can you persuade someone that it works?

Insufficient domain knowledge

You must understand how the business operates in order to formulate the appropriate inquiries.

The models you create should be targeted to an issue in a particular domain, and you should be aware of how they will affect the end user.

For instance, if you’re developing a model for a clothes business, you need to be aware that elements like seasonality will affect the consumer recommendations you make.

It is advisable to take some time off depending on the industry you work in to learn about.

Every step of your data science workflow will leverage this, including pre-processing, feature selection, feature weighting, and choosing whether to make modifications even after a model has been deployed.

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So, how can a data scientist do their work without hating it?

First and foremost, it’s crucial to choose a business that will let you work on the things you enjoy.

Avoid businesses that list every tool stack in their job description. Find these organizations on LinkedIn and see if they’ve ever been hired for a data science role before submitting your application.

If they haven’t, you should probably avoid them because you’ll almost certainly be forced to complete every data-related assignment there is.

If so, locate these data scientists’ profiles and look through the job description they provided. Check to see if your expectations are met.

Ensure that your interviewing procedure is not biased. Inquire as much as you can during the interview about the duties and responsibilities of the position.

It could be wiser for you to search elsewhere if it doesn’t meet your expectations.

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Finally, be sure to invest some time in learning the specifics of the industry you work in.

Utilize this information to ensure that your managers’ expectations are in line with the possible project outcome by asking the proper questions.