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Set These Boundaries for a Better-Quality Work-Life Balance as a Data Scientist In 2024

These 5 non-negotiables will help 2024 be your most balanced work year yet

Work-life balance is something everyone yearns for but only some have the guts to achieve.

With 2.9 billion search results for "work-life balance" on Google, it’s pretty obvious that it’s something we’re all after. Not only has it become a focus of our search efforts, but in the last three years it now seems to make its way into our everyday conversations.

In 2020, data science became seen as a career that could impart some of this mystical work-life balance that everyone is talking about. However, many seem to be realizing that working in some aspect of data science can be just as life-consuming as any other job, if not more thanks to our more prolific "flexible" work arrangements where bosses seem to feel the need to tighten their grip on us even more to soothe their micromanaging insecurities.

Unfortunately, a work-life balance is not always given. Sometimes it has to be taken, using boundaries and non-negotiables. With 2024 just two months away, now is the perfect time to begin preparing for how you plan on getting your work-life balance back to make this coming year your most balanced year yet. Here are the five boundaries you need to set for a better-quality work-life balance in 2024.


1. Prepare a documentation system

Inevitably, project milestones will be overshot, budgets will be strained, and timelines will be confused. When this happens, you may end up being the punching bag that takes the full wrath of your team lead, the client, or worse, your boss.

However, chances are also high that you were the only one who dotted their i’s and crossed their t’s. Therefore, it’s important for the sake of your work-life balance to create a documentation system that supports your ability, when things go wrong, to prove that you were the one doing your job.

One of my favorite ways to document these types of interactions is using a spreadsheet. There, you can create a simple document where each row is a unique incident/email/conversation (delete as required), with columns for information such as event ID (because we’re data scientists aren’t we, and not having this would just be chaos), date, name of the person you interacted with, the issue, their response, your response, whether this was followed up on, what the resolution was, whether escalation measures were needed, etc. I keep this kind of document open throughout the day so I’m assured that everything I encounter is written down.

As much as this may seem tedious or overkill, the one thing I’ve learned from working in tech that will always keep you from having to do more work than necessary is to document, document, document.

2. Make project timelines twice as long as needed

Anyone who has worked in tech for more than two minutes knows that projects always end up taking way longer than expected. That’s why, in the year when you will be getting your work-life balance back, you need to give realistic estimates of how long projects will take – in other words, always say that projects will take at least twice as long because they always will.

Data may be unexpectedly unusable, your team gets sick, your code may kill the software developers trying to make it production-worthy, your client may completely change their requirements one week before you’re set to launch, etc.

It’s imperative for the sake of your contracted hours (which, by the way, you should never work over, see below) that you set appropriate timelines for projects that allow you to produce quality work, even with all of the derailments, without forsaking your work-life balance. Nothing sends project quality down the drain faster than stress, which is why the careful work of proper data cleaning, analysis, and visualization is best done with a cool head and a deadline that feels weeks away. Better yet, if you manage to complete the project ahead of time, you’ve underpromised and over-delivered, which should really be your mantra as a data scientist.

3. Poor planning on someone else’s part does not constitute an emergency on yours

Sometimes, instead of the situation presented above where you get to set the appropriate project deadline, someone else sets it for you. And it’s unrealistic. And it’s going to affect your work-life balance to meet it.

Solution?

Tell people you will not meet their unrealistic deadlines and they should consult with you first to prevent issues like this from occurring again.

Oof. Yeah, I can see how that sounds scary to a data scientist who’s just getting started. However, I also know from experience that if you don’t stand up for yourself right from the beginning, it’ll be a heck of a lot harder to do later on, to the point where it may end up being easier to just leave the job than to try to rein in your ever-expanding set of responsibilities, projects, and unattainable deadlines.

Data Analysis is best left unrushed, even the tasks that seem minor, like changing the colors on a matrix scatter plot. While you will get faster with certain tasks over time, and automation may take on some of the heavy lifting, there is no point in producing half-baked results to a client who is depending heavily on the results of your analysis. The conclusions and resulting strategies you present could be life-altering for the client (not for the person, but for the company), which means that you need to be darn certain about what your findings are telling you. As such, you are not to be rushed. Everyone will be better off for it in the end, you might just need to remind them of that.

4. Never work overtime to artificial deadlines

By beginning to stand your ground on poorly planned deadlines as described above, you’ll already be making good ground on achieving this next non-negotiable. The next step is for you to never work overtime to artificial deadlines.

Artificial deadlines are a quick and easy way for you to suddenly begin checking your emails at dinner time, pushing code on the weekend, and having a team chat dissolve into another full-blown "quick" pair-Programming session.

2024 is the time for you to decline working overtime to artificial deadlines and instead save this sometimes necessary evil for only the most extreme and exceptional cases. But to be completely honest, when does a data scientist ever really need to work overtime? Very few times, though these times seem to appear when the data scientists are not just data scientists, but they’re also systems analysts, the local IT help desk for their cubicle cluster, and potentially also the software developer that makes everything production-ready.

The point is that to maintain your work-life balance, you need to be honest with yourself about the criticality of your overtime. Because as long as you’re in a healthy work situation within your company, there never really needs to be a "data emergency" complete with artificial deadlines.

5. Stipulate that quality is your only modus operandi

Clients want all three corners of the proverbial project triangle: speed, cost, and quality. It’s a fact of life.

As likely one of the few data scientists in your company, it somehow automatically becomes your job to enlighten the client about how data analysis projects work, and particularly why speed is never the answer. Sure the software department can slap together a product management system in a few hours, but thorough data analysis where business trends are being forecasted a year into the future should be done with some tact.

In other words, when your results may dictate where your client positions their business going forward, quality will never be abandoned for speed. It always ends poorly in the end. The client will benefit from you taking a few extra hours to refine your prediction models, especially if complex variables are at play, and more insight will be gained from an analysis that isn’t rushed for the sake of just getting something a couple of days faster.


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