3 Reasons Why Data Scientists Leave Their Jobs and How Tech Companies Can Change This

Data scientists are quitting their jobs in droves. While these employees are moving to other data scientist positions, this is a concerning trend among employers who lose their valuable employees. 

In this article, we will identify some reasons why data scientists are quitting their jobs and how employers can attract and retain more data scientists. Not only that, we’ll cover some tips for data scientists who want to land new jobs during the great resignation. 

3 Reasons Why Data Scientists Are Resigning in Droves

A study by 365 Data Science revealed that on average, in-demand data scientists will switch employers in 1.7 years. This is a worrying statistic for employers given the shortage of data science talent in the market. But what is driving the great resignation among data scientists? Here are three reasons you should be aware of.

Lack of Employee Engagement 

Low employee engagement is a leading cause of employee turnover for tech organizations. Low employee engagement means that your data scientists have low levels of enthusiasm and dedication to your company and their job. 

They do not feel the impact of their roles or care about their performance in a job. The result of employee disengagement is low productivity and high turnover rates. Employee disengagement can occur for a variety of reasons, such as:

• Unsatisfactory pay if the employees feel they are being unfairly compensated based on the industry, location, and market. 
• Lack of professional and personal growth opportunities
• Poor management 
• Stressful work environment results in burnout and reduced productivity 
• Employees do not feel a connection with the company’s goals, vision, and mission. Therefore, they cannot engage.
• Rigid organizations that do not adjust to change. As a highly evolving field, data scientists want to work on new and emerging challenges. 

Gap Between Reality and Expectations 

A few years ago, data science was considered the sexiest job of the 21 century. Companies rushed to adopt data science, AI, and Machine learning into their organizations, but in the process, they failed to determine their goals. 

In other cases, the employer will mislabel a role in the job description. For instance, a data scientist might be hired for a machine learning role, only to end up handling low-level analytics tasks. 

As such, companies will hire data science specialists, but give them responsibilities unrelated to data science roles. Professionals who land such roles become unsatisfied in their positions leading to high resignation rates. 

When employers gloss over data scientist positions to make them captivating for top talent, these employees eventually become unhappy and leave the company for better opportunities. 

Lack of Professional Development Opportunities  

Data science is a growing field. This means that the demand for data science specialists is increasing. At the same time, new opportunities and challenges are arising within the field, creating the need for data science professionals to keep up.

As with other technology fields, data scientists want to work with modern tools on new challenges that help them develop personally and professionally. 

However, without the opportunity for professional and personal development, data science specialists move from employer to employer until they find a company that supports professional growth. 

How Tech Companies Can Attract and Retain Top Talent Amidst the Great Resignation

Despite many data scientists leaving their jobs, the same study from 365 Data Science showed that some data scientists stay with the same employer for five or more years. 

Although it’s easy to focus on those resigning, the positive side of the great resignation is that these candidates are moving on to better jobs. 

This creates a perfect opportunity for employers to attract top talent to their tech teams. Attracting and retaining top data scientist talent is possible if you implement a few tried and tested tips. 

Here are some of these tips:

Be Clear on What You Want

Most data scientists will change jobs because their role does not match what they were hired for. This occurs when an employer hasn’t set up the right infrastructure or does not understand the role for which they are hiring.

To avoid this mistake, ensure that you understand what your business needs are and hire a person who is suited to that role. For instance, if you are hiring an AI expert, ensure that your business has the necessary tools to support the role. 

Cultivate Employee Loyalty

In addition to an attractive compensation package, employees want to work with organizations they identify with. This means that you should elevate your organization’s purpose and communicate this clearly to the employees. Here are some helpful tips when cultivating employee loyalty:

• Appreciate the efforts your employees make 
• Encourage employee engagement by providing opportunities for management and team members to interact daily.
• Provide constructive feedback to employees to help them develop their skills and abilities
• Introduce employee recognition programs to reward employees for major accomplishments
• Listen and act on the concerns your employees raise 
• Schedule regular check-ins with your employees to determine how well they are doing
• Invest in training and development opportunities 
• Communicate your company’s goals and vision effectively 

Provide Professional Development Opportunities 

Employers who focus on their employees’ professional development also have higher retention and engagement rates. 

Each employee has a different professional goal. Your job as an employer is to support your employees throughout their professional journey. This means that their roles should support their skills while creating new growth opportunities.

Here are tips to help you create professional development opportunities for your data science team:

• Create opportunities for one-on-one communication between managers and employees where they can share information 
• Involve your employees when setting professional development goals in your company
• Invest in training for specific skills to help your employees improve
• Provide room for upward mobility within the organization, where you promote from within. Provide development tracks that allow your employees to deepen their skills for the current and higher roles within the organization.
• Establish a regular training schedule 
• Support knowledge sharing to allow highly experienced employees to share their knowledge with the rest of the team. 
• Offer tuition reimbursement schemes to encourage your employees to seek training opportunities.

Invest in Work-Life Balance 

For most employees, the pandemic period provided a time for reflection on their priorities. This has led to some workers quitting their jobs to find positions allowing them to take care of themselves and their families. 

Therefore, investing in developing a workplace culture that supports work-life balance will go a long way in helping you attract and retain top data science talent. 

Embrace Remote Work

The pandemic forced companies to shift most roles to remote work. At the same time, data scientists enjoyed the flexibility of working remotely. As in-office work resumed, most people resigned due to their preference to work remotely.

A study by Microsoft shows that more than 70% of workers want flexible remote work opportunities, forcing businesses to consider adopting a hybrid work environment. 

Since this trend is expected to continue, employers who provide the flexibility to work remotely or in person will be an added benefit when recruiting talent. 

How to Find a Fulfilling Data Science Job During the Great Resignation 

Millions of people are quitting their jobs, with most being in the tech industry. With these resignations comes an opportunity for data scientists who are well-positioned to land rewarding career opportunities. 

Here is how to position yourself to land a better and more fulfilling data science job amidst the great resignation. 

Start with Your Goals 

The first step to take advantage of the great resignation to land your ideal job is to set your career and personal goals. Determine what you would like your career to look like. This is also the time to set your negotiables and non-negotiables. 

Are you set on remote work? Or are you open to a hybrid arrangement? If you have quit your job, you can establish your non-negotiables by highlighting everything that made you quit your previous job. Once you are clear on what you want, you can choose the jobs that meet your criteria. 

While setting your goals, review the current market for data scientists to see how the salaries fare. This way, you are in a better position to negotiate your salary and benefits. 

Polish Your Resume 

Just because data scientists are resigning their jobs in the masses doesn’t mean that you shouldn’t put in any effort. On the contrary, this is the time to polish your resume to increase your chances of landing high-value opportunities to grow your income and career. 

Here are some best practices to keep in mind when writing your data science resume:

• Keep your resume brief 
• Customize your resume to the job description and company for each application you submit 
• Include any relevant projects and certifications
• Highlight your skills and accomplishments  
• Proofread your resume 

Network by Doing Something Interesting 

Networking is an age-old method for landing new jobs. However, traditional network is outdated! Instead, if you are building something interesting then there will always be people that want to know you (including employers). Some examples:

• Projects
• Case Studies
• Vlog/Blog

Not the End 

The great resignation comes with significant disruptions and costs for employers. However, it is also an opportunity to evaluate your hiring process and workplace culture to improve the areas contributing to high turnover rates. For data scientists, the great resignation signifies a shift towards better opportunities and challenges within a rapidly evolving field. 

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