How to Secure Your First Data Science Internship

5 years back, I was frantically aiming for internships in the dark, throwing myself at every opportunity I come across. It didn’t work. I understand how hard it can get to land an internship because I’ve been through it. I have even paid for training courses that were branded as internships because I was unaware. I don’t even mention them in my CV anymore.

If you’re asked to pay for an internship, it’s not an internship.

Times have changed now, and the Data Science world is much different from what it was 5 years back. I am finally on the other side. I take interviews for prospective engineers, analysts, and interns for the data science team. There are certain things I, as an interviewer (and many other interviewers), look for in an intern candidate.

You don’t need work experience, but you need certain other things. These are certain things you have control of and can use to amplify the chances of securing an internship. This article talks about them in detail. Buckle up; your internship-hunt is going to get simplified into 7 simple steps!

1. Acquire skills

Start going after the skills right now! You really can’t expect to land an internship without acquiring the minimum relevant skills. Even if you land one, sorry, but you won’t be able to survive it. How do you acquire skills?

There is no single formula for that, but you can do online courses (there’s plenty for free) but remember it’s not the certificate you’re after, but the skills. You can learn from what experts have shared on Kaggle. There’s a lot of resources out there for free; you need to look for them.

Start going after the skills right now!

If you’re still stuck, here’s a guide I wrote a while back that outlines what you need to learn and where to find them, and the approach to data science in general. I highly recommend taking the time to go through this.

Do you need to be enrolled as an undergraduate at a university? Some companies require you to, but most are happy to hire you if you have the skills — demonstratable skills, to be precise.

Once you acquire the skills, how do you demonstrate them?

2. Create (at least) one demonstrable project

A demonstrable portfolio project is the single best way you can brag about your skills. It’s so convincing, so convincing when it’s right there for the interviewer to see!

Start with exploring datasets in Kaggle or UCI or go ahead and collect the data and build your project. A good way to think about a successful demonstrable portfolio project is by asking, does it cover all the typical steps of an end-to-end machine learning pipeline? Did you clean the data, treat outliers and categoricals, build models, evaluate them, and deploy them? Did you try to solve a problem effectively and able to communicate it well? If you could answer these questions yes, then you’ve got the project.

Don’t showcase something out of traditional getting started problems. Titanic Survival Prediction or Iris Classification, or MNIST Digit Recognition are good problems to get started and learn, but not showcase your portfolio project. Everybody does that, and you won’t be having a differentiating factor.

You might have already worked on such a project during your undergraduate program; you could fine-tune it and make it more presentable. The project should be on your GitHub, and you could even deploy it on the cloud for the period you’re looking for internships.

You have a demonstrable project; do you go ahead and apply to every vacancy online?

3. Stop applying online

When I was searching for my internship, I used to apply online. I believed the more companies I apply to, the better my chances. Since I am bulk-applying, I would use the same cover letter and the same CV for every application. I put a lot of effort into doing that, but I wasn’t getting noticed.

You get the problem right — I did what everyone else was doing, but I expected to be noticed.

Unlike Software Engineering, there is no definitive Data Scientist role; no two job postings are the same. Data Scientist role varies from company to company. And there’s more than data scientists a company would need. For instance, most companies also need a data engineer, data analyst, machine learning engineer, and the list goes on and on. And all of them work in the field of data science, and chances are you might enjoy it too.

You need to be genuinely interested in the company you’re applying to. Look in what domain do they work, what is their culture like. And depending on their requirements and vacancies, customize your CV and the cover letter. I can’t stress this enough, customizing takes you a step further towards securing the internship! But then how do you apply?

4. Get referred

Here’s an insider’s secret. The first internal step of any hiring process starts by asking the existing employees if they know someone good for the role. If your dream company's employees know you and have seen your potential, guess who is getting fast-tracked into the hiring process? By the time everybody else’s online applications reach HR, you’d be already holding the offer letter, my friend.

Wait, but how do I get referred? I know it’s easier said than done. But I’m here to tell you; it’s doable.

You have a genuine interest in the company. You have an idea of what they work on. You have built at least one demonstratable project. And you have the skills. It’s time you build a relationship with the employees of the company.

It could be as simple as asking for a review of your project (be mindful of their time, though). Or it could be asking for some tips for a future project you would be working on. Or you could simply inquire about the work they do at the company and if they’re looking for interns.

If there are no vacancies, don’t push it but ask them to keep you updated. It’s okay if an employee isn’t responding, there could be many other employees of the same company who is willing to. The best platform I could think of for you to do all these is LinkedIn. Leverage it!

When you show genuine interest in the company, the company, in return, wants to give you a shot at it! How would you make it count?

5. Prepare and practice for interviews

It’s very, very, very important to give your best in the interview. That’s the only way you get assessed, and also, don’t let your referrer down. Certain things you can do to feel comfortable are,

• Find out what to expect in the interview from glassdoor, geeks for geeks, or similar sites. You can also ask this from HR, too, when scheduling the interview. Generally, you need to pass-through technical and coding rounds depending on the company.
• Lookup for common interview questions and brush up on your theory.
• Know your project inside out and be comfortable explaining it from beginning to end (Feel free to brag about it as much as you want during the interview).
• Practice coding on common sites such as Leetcode and Hackerrank. You are expected to have some level of familiarity with problem-solving.
• Practice a mock interview with a friend, taking turns. It can help you to reduce the nervousness you might feel in front of the interviewer. You will learn how to frame your answers better when you practice answering this way.

Walking out of the room after acing the interview is the best feeling ever! You did your best in the interview. Now what?

6. Ask for feedback

It’s okay to ask for feedback from the interviewer once it’s over. It’s also okay to request feedback if you don’t hear from HR after a few days. Most companies don’t hesitate to give you constructive feedback (unless policies are forbidding them to), so ask.

If you couldn’t make it the first time, use the feedback to improve yourself and repeat the process. Based on my experience, after a couple of interviews, you will start noticing the same pattern of questioning and feel confident eventually. You are going to ace it naturally!

One last step. The important one.

7. Don’t give up

Sometimes you can do everything right and still not get the offer. It’s sad, but it happens. Requirements for the company may change, you might be caught in a pandemic, and they might freeze hiring, and so on.

Keep improving yourself, acquire even more skills, and have a couple of more projects. Build a stronger professional network. If I had given up, I wouldn’t be here, writing this. Have even stronger reasons for anyone to hire you!

Before you go

If you have come this far, thank you for reading. I hope it was useful to you in some way. Most importantly, please share this with someone who is embarking on an internship-hunt. Don’t hesitate to reach out if you need more guidance in any of these steps.

I write extensively on getting started with data science and machine learning, based on my personal experiences here on Medium. I’ll be more than happy to connect with you on LinkedIn and hear your feedback. I can’t wait to see you succeed!

For more helpful insights on breaking into data science, exciting collaborations, and mentorships, consider joining my private list of email friends.