How is the work life balance of a data scientist? (Updated for 2024)

How is the work life balance of a data scientist? (Updated for 2024)How is the work life balance of a data scientist? (Updated for 2024)

Data Scientist Work-Life Balance


Work-life balance is a common concern for new grads looking to become a data scientist or those making a career switch (example, from Finance to Data Science). They hear horror stories from tech workers about 10-hour days or 80-hour weeks, and think that’s the norm in data science.

That couldn’t be further from the truth. The fact is, the majority of data scientists and data analysts work 40-hour weeks, and have plenty of time outside the office for hobbies, relaxing, or a Kaggle competition if they’re so inclined. In fact, a data scientist is a top-rated job for work-life balance, according to Glassdoor.

Even so, work-life balance remains a concern for many aspiring data scientists. That’s why we decided to take a closer look and talked to several FAANG data scientists about work-life balance. Here’s what they said:

  • Depends on the company/team - Work-life balance (WLB) varies a lot by company, and even within companies, by team and manager. High-growth tech companies, in particular, can be very stressful and put a greater demand on your time.
  • Seasonality/critical deadlines - For the most part, the data scientists we talked to said they were satisfied with their work-life balance. But during peak marketing times, a critical launch, or when a “fire needed to be put out,” it’s normal to put in longer hours.
  • Work From Home matters - Especially nowadays, the freedom to work from home seems to be a critical factor in maintaining WLB. Many of the data scientists we talked to said working from home whenever they wanted makes it easier to balance work and life responsibilities.

Still worried about work-life balance in data science? Read on for tips to help maintain WLB, the WLB scores of top companies, and a look at work-life balance for data scientists versus data analysts.

How to Manage Work-Life Balance in Data Science

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

Ask Questions

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. If you’re really interested in the job, it might be best to frame the question in terms of company culture.

Another option: Scout out people in your network or do some research into company reviews to get a better idea of WLB.

Define Expectations

In a data science job, it’s important that you talk with your manager about what’s expected of you. If you feel that:

  • You’re being asked to do tasks ill-suited to your specific data science skill set
  • The company has unrealistic expectations of machine learning and what it can help the business accomplish
  • You notice PMs expect something different than what you can produce

You need to speak up for yourself and define expectations for your team. In FAANG companies with established data science teams, these expectations are usually clear, and there’s an overall greater understanding of what data scientists can accomplish.

This problem is much more common for companies with newer data science divisions. In these settings, you’ll have to define expectations, gain buy-in from stakeholders, and help the team understand how to best leverage data science to reach KPIs.

Defining expectations is very important. A recent Data Kitchen survey of data engineers found that 42% said unrealistic expectations were a problem for them:

Define Expectations

Be Prepared for the Culture

Start-ups and high-growth companies tend to set big goals and place high demands on their data science teams. You’ll likely be compensated well monetarily, but the trade-off is downtime.

Start-ups with big goals tend to have quicker deadlines and fast-evolving needs, and they do a lot of experimentation. That may make your job a bit more stressful, at least in the first three to six months.

Similarly, if you’re on a small team, or you’re operating as a single entity, you’ll likely have to balance data engineering and data analytics job functions. In these cases, it’s especially essential that you set expectations and clearly define your value to the team.

WLB and Career Level

Junior-level positions tend to work fairly fixed hours: 9-5 or 9-6 is pretty much the norm. As you progress in your career, however, WLB can get a bit blurrier.

For example, a senior data science consultant might be working on a project for a multimillion-dollar client, and speed to implementation is likely a key reason they hired the consultancy. In this case, WLB would likely be skewed, until the project is complete. The reward, of course, is compensation. Senior-level employees will earn significantly more than junior data scientists.

Work-Life Balance: Data Scientist vs Data Analyst

What’s the difference between the work-life balance of a Data Scientist to a Data Analyst? In terms of stress, data analytics positions tend to have a more defined work schedule. That’s because:

1. Analytics is generally strategic - Therefore, analysts tend to have more time to make decisions and plan.

2. Analytics platforms are 9-5 - Analysts use platforms that are primarily leveraged during business hours. Maintenance can be offset to off-peak hours, and there are fewer “fires” to put out.

Analysts do experience burnout. Although analytics jobs tend to be 9-to-5 gigs, again it comes down to expectations and the company. An analyst that is regularly asked to fulfill unrealistic requests will have trouble balancing work stress and life.

Top Causes of Burnout for Data Professionals No matter the job title, data science professionals share some common reasons for burnout. Here are some of the most common sources of burnout from the Data Kitchen survey:

Sources of burnout

In short, no matter the job title, WLB satisfaction comes down to the company and team. A data scientist working at a company that values WLB will be happier than a data analyst at a company with unrealistic expectations.

Data Science Companies: Work-Life Balance Scores

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, and these WLB scores do offer 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:

Work-Life Balance Scores

Find Your Next Data Science Job

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: