What Is the Work-Life Balance Like for a Data Scientist?

The focus on work-life balance has been the talk of the town in many industries, and for good reason. The work-life balance has a considerable effect on an employee’s happiness, engagement, and satisfaction, which then impacts performance and consistency.

Data scientists are particularly concerned about work-life balance in their field as the job is often described as demanding, especially since most of the time they are in front of a computer analyzing data and programming.

So, if you’re interested in building a data science career and want to know more about the work hours in data science, you’ve come to the right place. In this article, we will look at various sources and statistics to show what the work-life balance of a data scientist is like.

While the work-life balance is affected mainly by the company’s culture, it’s important to note that the perspective on this balance can also be individual. So, before we delve deeper into the work-life balance, let’s first look at the typical work hours in data science.

As in many other tech industries, most data scientists start their work day with a “standup” in which employees and teams gather to do a checkup on current and upcoming tasks. After that, a data scientist spends most of his day pulling, merging, and analyzing data to find patterns or trends and then shapes them into visual representations such as charts or graphs.

At the end of the day, a data scientist reviews their progress and prepares an agenda for the next day, and that’s about it. Furthermore, data scientists usually work solo and won’t have to attend too many meetings or collaborate with others a lot.

If this seems to you like a good balance between work and life, it actually is! In fact, data science is a top-rated job for work-life balance, according to Glassdoor.

With an idea of what a data scientist’s typical day entails, take a look at the common factors that affect (positively or negatively) the work-life balance of data scientists.

• Company culture - This might be the biggest factor, as the company or higher-ups will determine how the day will unfold for the data scientist. Usually, tech startups are not very forgiving and require a lot of hours weekly (more than 40), while larger and more established companies will have more structured and lax working hours.
• Project size and deadlines - Another huge factor when it comes to work-life balance is larger projects with tight deadlines. These lead to “crunch” periods in which data scientists may have to work longer hours and even have to be active outside of company time.
• Remote work perspective - Companies favorable toward remote data scientists usually have flexible hours, which leads to a more balanced work-life structure.
• Team size - The number of members in a team can heavily influence a data scientist’s work-life balance. With a smaller team, the data scientist’s workload can’t be offloaded to anyone else, while with larger teams, the workload can be distributed among multiple employees.
• Skill and experience - Data scientists new in the industry won’t have the necessary experience and skills to quickly and smoothly handle tasks, which can increase their work hours. Senior data scientists can efficiently finish their tasks, leading to more time. That’s why challenging yourself to continuously improve your skills is essential as a data scientist.

With these factors in mind, let’s look at how you can gain insight into the work-life balance within companies and effectively manage it.

Maintaining a work-life balance starts with you. Before and during employment, there are simple things you can do to protect and maintain your time outside the office. Here are a few tips:

Ask Questions During Your Interview

Starting with interviews, you can get insights on work-life balance and company culture right away. For example, you might ask: What does the company do to maintain employees’ work-life balance? Another option would be to ask a general question about company culture and values.

Asking about work-life balance is a very direct approach. Some hiring managers may perceive it as a negative, so tread carefully. Consider doing a mock interview to see how some people might take these questions.

Set Boundaries and Expectations

One mistake many people make, and not just in this industry, is that they remain active and available after work hours. It’s important to remember that your boss does not and shouldn’t have control over your entire day. If you respond to emails or messages during your free time or throughout the weekends, you’re setting a standard that will have a very negative effect on your work-life balance.

Setting realistic expectations plays another vital role in work-life balance. Impossible deadlines or getting assigned tasks that are not within your skill set can happen. In fact, a recent Data Kitchen survey of data engineers found that 42% said unrealistic expectations were a problem for them:

Be sure to voice your opinion when you face unreasonable requests.

Task Prioritization

One useful philosophy for managing the balance between work and life is to prioritize and distribute your tasks accordingly. Handle harder and high-impact tasks in the morning and leave simpler and not-as-important tasks for the end of your day. With this kind of schedule, you use your well-rested and fresh mind on more complex matters, and then you can have a more low-key afternoon, reducing the amount of stress you bring home.

Work On Improving Yourself Professionally

As we mentioned previously, one factor that determines your work-life balance as a data scientist is your skill. As your skills grow, you will be able to solve problems more efficiently, reducing stress and allowing you to finish your tasks for the day early.

Considering that the field of data science is constantly evolving and at the forefront of technology, you should continuously learn and develop your skills as a data scientist to stay ahead and improve your work-life balance.

Prevent Burnouts

Burnout is not uncommon in the data science world, disrupting and indicating problems in your work-life balance. To manage your work-life balance, implement strategies to prevent burnout. Learn the most common sources from this Data Kitchen survey:

One of the best ways to determine work-life balance for a data science job is to research the company. On sites like Glassdoor, employees rate their employers on work-life balance. These WLB scores offer helpful insights into the company culture.

Of course, there may be bias, as the most overwhelmed employees are more likely to rate their company. But if you read plenty of reviews on Glassdoor and Blind and reach out to people in your network, you can get a pretty clear idea of expectations and how much respect the company has for their employees’ time outside the office.

Here are work-life balance scores for top data scientist hirers:

1. Google - 4.³⁄₅
2. Adobe - 4.³⁄₅
3. Microsoft - 4.¹⁄₅
4. Oracle - 4.0/5
5. DoorDash - 3.⁸⁄₅
6. Meta - 3.⁷⁄₅
7. Netflix - 3.⁷⁄₅
8. Apple - 3.⁶⁄₅
9. Accenture - 3.⁶⁄₅
10. Amazon - 3.⁵⁄₅

The work-life balance of a data scientist depends on a lot of factors, but most importantly, the company culture and your work habits. Even though statistics show that data scientists typically enjoy a good work-life balance, it is still vital to understand what negatively influences it and how to maintain that balance.

We also recommend carefully selecting companies when looking for a data science job which can give you insight into the companies’ work-life balance policies. Also, make sure to prepare well technically by working through questions via coaching sessions to make the best out of your interviews.

If you’re in a data science job and unhappy with WLB, it might be time to consider a change. Interview Query offers a variety of resources to help you land your dream job:

• 500+ Real Data Science Interview Questions
• Data Science Courses
• Company Data Science Interview Guides