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 42% said unrealistic expectations were a problem for them:
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.
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