I started my career in 2021 as a Data Science intern at Spotify.
Then, I did another internship, and then a master’s thesis as a machine learning researcher.
So I’m something of an internship specialist myself. My colleagues even used to joke about the fact that I was becoming a senior intern. So if you’re a student dreaming to join Spotify, this article is for you.
The year I got accepted, there were around tens of thousands of applicants. Needless to say, it’s pretty competitive.
And it’s easy to see why.
For many of us, including myself, Spotify is a dream company. Nothing resonates more with people than music. So many students dream to join the band and for a good reason.
Landing an internship is often the best way to get a full-time offer when you’re a graduate, even in tech.
Two years ago, I happened to be one of the lucky ones who got an offer to stay, and ever since then, I haven’t stopped receiving messages of students asking me how I did it. It’s the #1 question that comes the most.
So today I’m finally sharing the story of how I landed one of the most competitive and coolest Internships in data science. I’ll address:
- The application process
- The resume-crafting step
- The recruiting process with the interviews
I will not only be sharing my story and the challenges I met along the way, but I will also provide you with tips to set you up on a similar path. From student to student. I’m no longer one, but just pretend I am while reading this story.
Make sure to watch the video too! You can hear me tell the story, plus you’re in for a laugh watching my weird editing skills (will do better next time, promise!).
Part 1 – The Application Process
Landing an internship within the most competitive tech companies can often seem like an impossible challenge.
I have a lot of experience in facing rejection.
I remember applying to internships at Spotify for many years in a row. I did it every year for 3 years and every year for 3 years I got rejected.
In 2021, I decided to give it another go. Except this time, I felt I might stand a chance because of how much I had levelled up my technical skills.
People always ask me how I applied __ to Spotify and made it work.
Here’s how I did it
Step #1. I meticulously crafted my resume. I tailored it to Spotify specifically. More on that below.
Step #2. I poured my soul into the cover letter. Not sure if it was read, and we’ll never know, but we can’t rule out the probability it might have helped. So do your due diligence and write one too!
Step #3. I applied to 6 of their internships, 5 of which I got rejected from. More specifically, 3 in data science and 3 in machine learning engineering, in all 3 available locations – NYC, London, and Stockholm.
Doing this increases your odds of landing at least one because internships are usually offered by different teams. I’d have applied to more if there had been.
Step #4. I filled out the application form on Spotify’s public job portal and pressed send. Six times.
That’s all. No referral letter. No blood sacrifice. No dancing kumbaya. No nothing.
Getting in through the conventional route is not a myth or a lie. All employees at Spotify have gotten through the same public job portal. And it’s amazing because it keeps the process fair for everyone.
What about referrals?
Sometimes if you’re lucky you might get a referral from an employee, but that itself is not a guarantee you’ll get the job.
What matters is what you do with the opportunity itself.
I’ve referred a few students for internships at Spotify but none of them never got one. The big majority of those who succeed don’t have referrals.
What matters is whether you’re skilled or not. So apply only when ready.
Crafting a killer resume is the most crucial step
I spent years crafting a killer resume.
I can’t tell you the number of resume workshops I attended in my life. If this career doesn’t work out, I can probably make it as a resume career coach lol.
I quickly understood early on that competition for data science Jobs is crazy (you’re like fighting for your dear life out there). The only chance you stand at making it out is by making a great first impression, and it starts with your resume.
It’s what you give as face value for who you are. These people don’t know you, so you have to show them how special you are.
If you send a generic CV, you’ll go unnoticed. There’s a 100% success rate that you’ll fail.
Investing time in learning how to design the best resume is crucial. It paid out for me because at the end my resume strategy landed me in the 0.1% accepted applicants at Spotify.
I share all about this strategy in the article below. I walk you through the resume-making process in detail using my Spotify resume:
In the meantime, here’s a summary of what to keep in mind when designing a resume that can turn heads:
- Tailored & creative approach – To stand out, your resume needs to be customized for the specific role and company to which you’re applying.
- Relevancy of your experience – Only highlight the experiences/projects that are relevant to the position you’re applying to. No one cares if you placed first on the Titanic Kaggle project if you’re applying to Meta.
- Business and Technical skills for the win – Your experiences/projects should speak for your ability to balance out business objectives with technical expertise.
- Balance of soft & hard skills – There should be enough evidence to indicate team collaboration, leadership, and other soft skills, on top of your technical stack.
- Formatting matters – Be mindful of the design of your resume, it should be well-organized, easy to read, and consistent. Your story needs to flow naturally from top to bottom.
- Show, don’t tell!
Business skills for the win
When I joined Spotify, I discovered that one of my colleagues had personally selected my resume from a stack of others. So I asked him why.
He said I had a dual background in both business and data science, and that was a powerful combo the best data scientists he knew had. He saw that potential in me.
The lesson from this? Emphasize your business know-how alongside your data science skills. Tech companies value people who are proficient in leveraging data to further business goals.
In the end, I heard back from one data science internship position only, the one in Stockholm, and I made sure I wasn’t gonna let it slip away.
Part 2 – Acing the Interviews
Culture Fit Interview
My first interview tested my fit within Spotify’s unique culture. I went with a chill mind and decided I’d just be myself.
At the time, I had already secured 2 other internships – one at Ubisoft, and the other at some tech consulting firm – so I could afford to have that peace of mind.
I wasn’t burdened by the stress of having no plan B. So make sure to always have other options to fall back into in the event your interview doesn’t pan out.
That day, I got asked many questions, but to be honest, the only ones I can remember were:
- Why did you want to become a data scientist?
- Tell me about your past experiences.
It’s been 3 years after all. But still, it changes nothing to the fact that:
The key to culture-fit interviews is to leave space for spontaneity.
I didn’t want to pressure myself too much because I valued being able to showcase my authentic self. Too much prepping would hinder that.
Faking being something you’re not for the sake of an internship will definitely show on the interview, and will feel unnatural to the interviewer, so don’t do that. You might be hurting your chances more than anything.
For me, it worked in my favour.
The recruiting manager (who later on would become my data science manager) was surprised by my unconventional answers.
"So why did you want to be a data scientist?" he asked.
"Oh well, I’ve always wanted to be a detective, but I missed that train long ago, so I just figured it was the next best thing in playing detective" I said.
He actually laughed, and told me he’d never heard anyone else give that answer. From there the conversation just flew more naturally with ease.
That’s why it matters so much to be yourself.
It means you’ll be free form the pressure of fitting into a specific persona.
Being authentic matters more than we think because companies don’t just hire competent people, they hire people they’d also enjoy working with. Normal, we’re humans.
And Spotify is full of authentic and unique people, so you really can’t go wrong doing that.
So my advice – don’t overprep the culture fit interviews. Don’t be scared to let your individuality shine through. That’s what will make you stand out.
Just make sure you know how to answer basic questions about your strengths, weaknesses, and whatnot, in case these ever come up.
Then he asked me about my previous experiences and my school background. The only thing to prepare for here is projects. Just make sure you know how to talk about:
- Tools used
- Methods applied
- Leadership role you played
- Quantified impact of your role
It paid off because I got moved up to the next stage.
Coding Interview
Three weeks later the coding interview came in.
I just practiced some SQL exercises on HackerRank, reviewed Python code and the maths basics.
Nothing more and that was enough.
On the day of the interview, things suddenly went unexpectedly bad.
It went so bad I thought I’d never get a call back.
The interviewers were two data scientists from the recruiting team. They showed me the exercise, and cued me to get started. It was a mix of Python and SQL of standard difficulty, nothing scary really. I could even google the code.
At first it went well, but the situation quickly took an unexpected turn.
Something went wrong.
I could barely hear myself think.
Everything just went blank.
That day, was the only god-forsaken day where construction work was being done on my student building on the floor right below. Even worse, it started right when my interview was about to begin.
The banging was so loud it might as well have been happening in my room.
I got extremely unsettled and started to go into panic mode. How could my timing be so bad?
They could barely hear me speak with all the noise. It was like construction was happening in my room. So in the middle of the interview, camera on, I went to the kitchen hoping for some peace and quiet.
But there was none to be found.
I started messing up even the simplest of tasks like doing group by’s and whatnot. However, the interviewers saw me going on the verge of a system crash and reassured me.
Instead, they asked me to focus mainly on voicing the thinking process rather than getting the answer right. I was asked to interpret p-values and coefficients from a linear regression, and that was about it. Really, just like any other technical data science interview.
After that the interview was over.
I was convinced I’d never hear back from them.
But I did.
The lesson here? There are many actually.
- Problem-solving abilities and thought processes matter more than your coding skills. Knowing how to code is a basic requirement and the last thing that would set you apart in an interview. These companies are looking for analytical thinkers not programmers. Besides, everybody can code nowadays.
- Review your maths basics. You need to be able to answer questions like: What’s a p-value? What do the coefficients mean? What’s overfitting? What’s linear independence? What’s a true positive rate? False positive rate? What’s statistical significance and how to verify it? What are the different statistical tests and how do they work? and more. Those are the questions that usually come up in most interviews of tech companies.
- Try not to lose your way if the interview doesn’t go well. Focus on doing as best as you can and explain any setback you may be experiencing during the meeting. A friend of mine once came 15 min late to an interview and had his computer run out of battery in the middle of the call. He thought he’d never get the job, but believe it or not, he still did.
Part 3 – Getting the Offer
A few weeks later I got a call from the recruiter announcing me I was the one who had landed the internship.
Against all odds.
The day I got the call I was studying for exams and received an email from the HR representative asking me to plan a call on the same day to give me updates.
I genuinely thought I was about to gently be dismissed, but never have I been so happy to be so wrong.
When he told me I had gotten picked, I jumped from my seat and went bouncing all over the place. I shed a few tears of joy and kissed the floor thanking God (totally not dramatic), but hey it was a dream come true after all.
I have to admit that even after three years, that day remains one of the most memorable days of my life. It felt like the culmination of a long and challenging journey in data science that was finally paying off.
I had spent the previous months piling up rejections even by the most random companies, so I never thought I’d be landing my dream job only a few months later.
It felt like I was getting revenge on all those people who didn’t believe in my potential.
End Word
Believe in yourself. Sounds cheesy, but it helps for others to believe in you too.
I truly think hard work always pays off, sooner or later.
The career of a data scientist is surely a challenging one bumped with many challenges, but the end goal is so worth the struggle.
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