5 Essential Lessons for Junior Data Scientists I learned at Spotify (Part 2)

The First-Years Chronicles of a Data Scientist in Tech

The insider’s guide to crushing your first years as a data scientist in Tech and leveling up your game

This article is part two of the “The First-Years Chronicles of a Data Scientist” series. Make sure to check out Part 1 first!

So previously, we discussed :

The importance of sharing your work regularly with stakeholders, even if it’s not finished yetSeeking feedback regularly to ensure you’re on the right track

Doing this will help you build trust with your team and stakeholders to make sure your work has the impact it deserves. Talking about trust, let’s jump right into lesson #3.

Lesson 3 — Start Building Trust

Making an impact is all about pushing your ideas to the people who will turn them into actions.

These people are usually product managers (who build the vision and strategy for a product) or designers (who design the product). The insights you will share and recommendations you will make, will be driving the work of the whole team (product managers, designers and engineers).

So it comes with no surprise that you need to learn how to persuade people why they should listen to your ideas and why they matter in the first place.

Welcome to lesson #3!

It might seem obvious, but being trustworthy is central to the role of data scientists and it’s a skill that gets honed with time. But you might ask me ‘K, how do I do that? I’m still a puppy, who will take me seriously?’
Hush hush, a company does not hire you unless they trust in your ability to be reliable in the first place. So people are most likely going to be trusting you already, now the key is to live up to it.

So how can you build the first layers of trust when you’re only getting started?

We’ve already discussed how communicating your work and asking for feedback can boost your trustworthiness, but let’s see what else you can do!

1. Be proactive & ask questions whenever possible

One natural talent I surely was born with, was that I have a knack for overly showering people with questions they didn’t ask for. This might not have worked in my favour in high school, but the professional world, and in my case, tech (the only one I know), plays by different rules.

One thing I have found myself to be repeatedly commended for, was my ability to twist a topic from all sides with questions (I’m an ENTP duh, that’s the only thing I can do anyway).

Showing that you’re interested in understanding how something works, and why, will definitely set you nicely on your way to being a trusted source of truth and decisioning.

2. Be humble and don’t be afraid to say it when you don’t know what you don’t know

Yes, you can read that again. Don’t be like Jon Snow. If you don’t know how to do it, no need to pretend you do. If anything, this can hinder you in the long run.

Humility generates trust, that is why being humble in your insights, and putting disclaimers when needed is paramount.

As newbies, we can often be tempted to not show when we don’t know. However, working with data and statistics ultimately means that results are not absolute. In practice, being vulnerable about the level of confidence you have in your results, can be hard but it’s important. Be transparent about the robustness of your work!

How can you do that?

Making sure you communicate that you’ve been actively trying to find solutions is the first step towards growth. No one will blame you for not getting things right, but I will blame you for not trying at the very least.Showing that you’re actively involved in your own growth will definitely boost your trustworthiness and reliability in the eyes of all beholders.

Solutions enjoy playing hard to get. So good thing we’re data scientists, because we do love digging up for those hidden insights, don’t we?
It’s only this way that you’ll be able to learn from others, and improve yourself.

Lesson 4 — Ask Experts for Help

Photo by Nathan Dumlao on Unsplash

And maaaan the time it would have saved me had I learned this sooner rather than later.

Picture this:

I’m working on a causal inference project (field of statistics that aims to identify the causal relationship between variables based on observational data). I had taken the course in college, but I couldn’t seem to recall much from it (maybe I was sleeping again this time). So anyway I’m working on this project, tackling new exciting concepts to tickle my brain just like I like it.

I’m asking my closest peers for advice, diving back into past projects, hoping to find some inspiration, learnings, tips, god’s word… really anything that can help me. So yes, this one is also an important skill to have under your sleeve:

Tracing back into past resources should always be a first step when starting new projects

but I’ll dive more into this in another story.

So I’m doing my research, investigating internal and external resources and I do find myself butting heads with causal inference (normal, that’s a tricky one). I do all the right things (or so I thought). I go on and on working on this project, and after some time… I come up with the great idea of asking other data scientists for clarification on a concept.

By doing that, I obviously provide more details on my project. When… out of nowhere… the word of a heaven-descended causal inference expert shone down upon me… to nicely let me know that I was… barking up the wrong tree. My whole methodology was off track because I was comparing two populations of users that cannot be compared, which throws off the whole analysis.

When I knew I’d messed up — Photo by Jelleke Vanooteghem on Unsplash

And there, my friends, that’s how weeks time of work earns a one-way ticket to the trash! (Well not completely, because this becomes a core memory in your brain and a nice lesson to live by)

Which brings us to lesson #4 — Learning how to overcome challenges on your own is important in developing your critical thinking and problem-solving skills. But learning to ask for help whenever needed is also as important. People go right to ChatGPT now for help instead of giving it a shot on their own, and this ultimately prevents them from learning the right skills.

Asking for help from the right people has valuable benefits

1. Getting firsthand guidance from experts in whatever you’re working on

Clearly this can only but give you a boost: a) for your project and b) for your skillset. Remember, you might be a puppy but you’re also playing in the same sandbox as experts in the field, so don’t forget to ask for guidance when needed (unless you’re planning on staying a puppy for a bit longer, then that’s another story).

How can you do that?

Look for past projects where what you are working on has been implemented or researched. Then reach out to the people who worked on them. Chances are, they will definitely provide you with valuable information and help you identify potential inconsistencies.Send a message on the Slack/Teams channels dedicated to the problem/technique/feature/product area etc… you are working on, eg. #causal-inference or #data-science (with a wider net, someone will definitely take the bait)!

Everyone is always happy to help. After all, they’ve been there themselves.

2. Spare you the frustration of realising you’ve been doing things wrong

This will ultimately also save you from the stress of having to work extra to make up for your past mistakes… because now, you’re also running late on your schedule.

Just don’t forget to at least give it a try first. If you find yourself getting stuck longer than you should, then you know it’s time to seek for a helping hand.

Last one for the ride — Be patient with yourself

Photo by sydney Rae on Unsplash

If you’ve gotten all the way through here then you definitely deserve one extra cookie for sticking around. Thank you for reading me.

Brace yourself, now I will bestow on you my ultimate cookie for this ride.

No one expects you to be an expert from day 1 or even day 100

Even in Tech! Working alongside experienced people is a unique opportunity to grow between good hands. However, it did take me some time to be fully okay with being the most unexperienced one in the room.

I looked up to the people I worked with, but I also subconsciously compared myself to them:

It would take me much longer to deliver my workI would not always ask the right questions to explore, so it felt like my exploration was limited in scope compared to my peersI would struggle with juggling between more than 1 project at a time, while others felt like they were smooooth sailing through 5 tasks at a time

Yes, it might seem obvious, but it wasn’t that obvious to me at least. So if like me, at times you can be hard on yourself. Striving for the best is important. However, know it’s okay not to be the best when you’re just getting started.

I mean come on, you’re a puppy, no one expects you to be on the same level as fully grown wolves anyway. But you’re in the pack, and the pack does not leave its own behind. So don’t worry, you’ll howl too one day, it’s only a matter of time, and commitment.

Getting everything right from the beginning would actually not be the norm. Besides, surfers do not ride still waters, where’s the fun in that? Even Harry Potter didn’t get his Wingardium LeviOsa right from the get go.

So what can you do?

Avoid overdoing it by trying to imitate your seniors. Chances are, it will not feel natural, and people will sense it. Instead, ask questions without forcing it and be yourselfReach out to other fellow juniors. Exchanging with other people who I could relate to definitely helped me get more perspective when I was struggling with this myself. It does not have to be other data scientists, any junior you’re closing enough with will do. Knowing that you’re not alone and finding support changes everything.

So last lesson: Cut yourself some slack and stop putting that extra pressure on yourself if you’re doing that. If not, then this lesson might not be for you, but it’s still worth to keep in mind and remember to be kind to yourself.

If you’ve enjoyed this article or found it useful, then please follow me for more stories on my journey, or leave a clap so other people will see this here on Medium. And if you just know someone who might find this relevant, then please do share it!

5 Essential Lessons for Junior Data Scientists I learned at Spotify (Part 2) was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.


Oh hi there 👋
It’s nice to meet you.

Sign up to receive awesome content in your inbox, every month.

We don’t spam!

Leave a Comment

Scroll to Top