Productivity Tips for Data Scientists
Productivity Tips for Data Scientists
How to work better, smarter but not necessarily harder as a data scientist
How to work better, smarter but not necessarily harder as a data scientist
I have always been a firm believer of “work smarter not harder” and working in fast-paced companies in the past several years reinforced this belief. Based on my observations as a data scientist, there seems to be a permanent discrepancy between how “slow” analytics (or engineering) work goes and how fast businesses need solutions.
Even though this tension is mainly due to the fact that highly technical work has the level of difficulty and needs the level of precision that takes time, it can partially be alleviated through proper stakeholder management (which I have covered in several other articles) and by making yourself a more effective and efficient data scientist. There are several lessons I learned in the past several years that I hope can help you make your work as a data scientist “smarter”.
Help others to help you
If you have ever gotten questions along the lines of “how many XX (transactions, trips, etc.) did we have last month?” (what I call “data Siri questions”), you know how much time you spend on those seemingly small asks and how frustrating they are.
The best way to NOT let these small asks drag down your productivity (and mood) is to up-skill your stakeholders so they can be self-sufficient to some extent.
Most stakeholders I have worked with are more than happy to learn the basics about analytics in a weekly or monthly office hour or training session the data team hosts, because nobody loves to rely on other people all the time to get their jobs done.
Be that “annoying” data scientist and ask “where does the data go”
Time and time again I observe the data team scrambling to get data when people ask “can we track XXX (related to a product or process recently launched)?” and the data team realizes the data is not being tracked correctly or at all.
My solution to avoid this kind of situation is to “butt in” the conversations early. When you are in a meeting where people talk about building a new product and/or process, if you know you will eventually be responsible for analyzing the data (that’s probably why you are in the meeting as a data scientist), ask “dumb” questions as early as possible “where will the data be stored?”, “have we decided the schemas of the tables yet?”…
I used to assume someone must have already thought of those questions. Don’t make those assumptions. You will be surprised by how often people forget about the data aspect of things, even in companies that pride themselves for being data-driven.
And trust me, people will appreciate you for asking those “dumb” questions early on as it will avoid bottlenecks down the road.
Pay attention to “irrelevant conversations”
Being able to absorb information about things that don’t seems to fall directly in your immediate scope is an underrated ability in my opinion. Time and time again I realize that conversations I read in Slack threads and group chats that didn’t seem to concern me at the moment contain useful information for my future work.
It’s really not surprising if you think about it. Every piece of work that happens in the company is intertwined and connected because at the end of the day, everyone is working towards the same goal. It’s only a matter of time before things that are not relevant for you become relevant; and when they do, you have some basic context and know what’s already been done or at least where to start looking for information.
So be curious and inquisitive of things others are working on and don’t be tunnel visioned by your scope. The future you will thank the past you for catching certain pieces of “irrelevant” information and saving a huge amount of time because of it.
Be a sponge and a dot connector
This one ties back directly to the one above because you can only connect the dots if you paid attention to things that are happening around you.
Being able to absorb information and connect the dots is one of the most valuable abilities I look for in people I hire. I can’t even start to describe how much I appreciate team members who can tell me “XX mentioned that he/she did YY that I think is very similar to this new ask”.
Like I mentioned in my previous post, when it comes to high impact projects, it’s likely that someone has thought about and done something similar in the past; so being able to NOT reinvent the wheel but build upon previous work can save a lot of time and create a ton of synergy. And if another team is currently actively working on a similar project, you can either join forces or simply reprioritize and work on something else while they solve the problem for you (assuming that the timeline and output align with your needs).
Batch your work and utilize productivity-hacking
Unless you have an extremely micromanaging manager, you usually have some control over how you prioritize your work. The best tricks for being more productive that I have learned over the years are targeting low hanging fruits at the right time and decreasing switching cost by batching similar work.
Even if you successfully up-skill all of your stakeholders, you will inevitably get some “data Siri” questions that shouldn’t take much of your brain space, so use those to fill the hours when you feel like your brain is fried and you can’t really concentrate, or when you are in a boring meeting and you can multi-task.
Batching your work is another way to improve productivity because switching back and forth between different topics can be distracting and result in inefficiency and slow progress. So I usually group similar tasks together and knock as many of them out as possible in one sitting. Schedule all of your meetings back to back and reserve several hours afterwards for headphones-on, heads-down, productive coding time. Your brain will thank you for not pulling it in all directions in a short span of time.
Never dive into a problem without taking a step back first — question everything
Don’t let others’ approach for a problem be the “box” for your thinking process. Think outside of the box. What I mean by this is if your stakeholder has an analytics request that they have some ideas about how you can approach it, that’s great, hear them out; but don’t let that be the Bible for how you MUST approach it.
As a data scientist, you should have the data expertise in the room and can help people decide what the most efficient way to approach a problem is from a data perspective. Don’t be afraid of challenging the approach and making suggestions.
Key Takeaways:
There are several ways to make you more productive and more effective as a data scientist:
Up-skill your stakeholders to lighten your ad-hoc requests load
Be more proactive in data-related conversations
Take more initiative in /pay more attention to conversations that are seemingly not in your scope
Learn to connect the dots
Prioritize by batching your work and breaking down big tasks
Don’t take anything for granted, always look for more efficient approaches
Enjoyed the article? Subscribe to my email list so you won’t miss an article in the future! Don’t know what to read next? Here are some suggestions:
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