Data science is a competitive field as it remains a preferred career prospect; in fact, the employment rate is expected to grow 36% by 2031. To successfully land a job, you must prove you know how to manage the responsibilities. Having practical experience on your resume makes you stand out to employers as a capable candidate who understands how to work with real-life data. And a way to gain experience is through internships.
This article shares advice on how to get a data science internship as a student or someone transitioning from a different field.
Table of Contents
- Where to Look for Data Science Internships
- What Skills Do You Need for Your First Data Science Internship?
- Do You Need Experience to Get a Data Science Internship?
- How to Write a Resume and Cover Letter for a Data Science Internship
- How to Build a Portfolio for Your Data Science Internship
- How to Build Your Personal Brand to Land a Data Science Internship
- What to Expect as a Data Science Intern
- How to Get a Data Science Internship: Next Steps
Where to Look for Data Science Internships?
More companies are extending internship opportunities for aspiring data scientists to gain relevant industry skills in a real-life setting. Here are some sites you can start your search.
Browse Online Job Boards
Companies that hire data science interns will often advertise the opportunities on online job boards, including:
- Indeed
- Glassdoor
- Untapped
You can also browse specific data science job boards. Some noteworthy ones include KDNuggets, DataJobs, and iCrunchData.
Large companies like Meta, Google, and Amazon also offer data science internship programs they advertise on their websites. But keep in mind that they’re highly competitive. Of course, that shouldn’t deter you from applying because work experience in a prestigious company will look great on your resume.
University Boards
Remember to consider your institution's job boards if you're a student. Sometimes firms announce specific job openings exclusively through the university portal. Only a few pay attention to this source—so keeping an eye on it increases your chances.
Startups
Be proactive and contact intriguing startups. Generally, these firms are focused on setting up their business; they usually are looking for fresh perspectives and won’t have as many internship queries as bigger companies.
As a data science intern in a startup, you’d have more varied tasks than in a more established company with advanced processes. Working within such a team would be great for your personal development, and you’ll gain valuable experience working in a dynamic environment.
Data Science Conferences
The benefit of visiting data science events is that you network with professionals in the field. If you talk to the right people, you can expand your connection list and invite work experience opportunities. It’s a time-efficient process, and you can make a good impression by showing off proactive motivation.
Data Science News
Suppose your future employer asks you about the latest data science article you read. You don’t spend much time browsing the internet for data science news, but you open your email once or twice a week and read the newsletters from such websites as SmartDataCollective and KDNuggets. The names of these well-recognized platforms are memorable, leaving an incredible first impression. And, of course, the more you read, the more up-to-date you’ll be—and the more bonus points you score with your future employer.
What Skills Are Necessary for Your First Data Science Internship?
While you don’t need professional experience to become a data scientist intern, most employers look for individuals with domain knowledge, capabilities within the data science sphere, and desirable transferrable skills.
Let’s look at what will make you stand out.
Technical Skills
Your responsibilities as an intern will be part of the overall data science workflow—so you must be prepared to work with industry-standard tools and techniques. Our research into the data scientist job outlook showed that the most in-demand tools in the filed are Python and SQL. Other frequent requirements for internship candidates that appear in job postings include the following:
- Mathematical and analytical capabilities for data analysis and data manipulation
- Spreadsheet and functions proficiency for working with datasets in Excel
Occasionally, some ads list machine learning and business acumen, but these are more advanced domains you’ll learn as you grow into your role. Nevertheless, understanding them as early as possible is a good idea.
An excellent way to build your skills if you’re not from an academic data science background or have an adjacent degree is to take online courses and earn certificates for your resume. We recommend our data scientist career track, which starts from the building blocks of data science and introduces more advanced topics as you go.
Remember, your skills don’t need to be perfect, but having a good grasp of the theory and tools will impress your employers and give you a competitive advantage in the job market.
Soft Skills
Employers also look for essential soft skills when hiring data science interns, including:
- Strong communication skills (written and verbal)
- The ability to translate complex technical topics to non-tech stakeholders
Finally, being proactive and showing a desire to learn will leave a good impression on your internship supervisors and open opportunities to expand your toolkit into broader data science techniques.
Do You Need Experience to Get a Data Science Internship?
You don’t need formal experience to acquire a data science internship—after all, that’s why you’re doing it in the first place. An intern’s fresh perspective brings new and innovative ideas to any team. “Companies can use this perspective to their advantage by working closely with interns to develop and test new hypotheses,” says Eric Frenkiel (co-founder and CEO of database startup MemSQL).
Employers often require you to be a student or have higher education (bachelor’s or master’s degree)—typically in data science, computer science, statistics, or an adjacent degree in the industry they’re working in.
If you’re switching careers from an unrelated field, check out our guide on How to Become a Data Scientist with No Experience.
How to Write a Resume and Cover Letter for a Data Science Internship
The key to landing an internship in data science lies in a well-presented application. After all, your resume and cover letter will be at the fore of potential employers—so make it count.
The sooner you start building your data science resume, the better. You need to ensure it’s up-to-date and includes previous projects¾we’ll talk about those in the next section.
Tailor your application to the company you’re applying for an internship with, which should include the following:
- Customize your resume to fit the job spec—but only if you meet the requirements. (Lying on your application is bad practice.)
- Include keywords from the posting in your cover letter to show you understand what the company needs from interns and demonstrate you have what it takes.
- Highlight your technical capabilities and how you’ve earned them, including your formal education and certifications.
- Emphasize the transferrable skills that make you a good fit for the role—even if you’ve developed them in a non-data science-related capacity.
Employers may not pick a generic resume and cover letter from the vast pile, but a customized one will ensure you stand out from the other applicants. Suppose you'd like to learn how to tailor your cover letter for success. In that case, you can read our dedicated guides on the topic:
- How to Write a Winning Data Science Cover Letter
- How to Organize a Data Science Cover Letter
- How to Format a Data Science Cover Letter
- Data Science Cover Letter Dos and Don'ts
Another tip is to be aware of appropriate interview etiquette to have a better chance of landing the data science internship. Practice possible interview questions with your friends—making you feel more confident and well-prepared.
How to Build a Portfolio for Your Data Science Internship
A robust portfolio is an excellent way to land temporary work experience or a permanent position. Employers like to see you know how to implement your skills in real-life situations. And projects are a great way to prove that.
So, where should you start? The following addresses the best ways to work on data science projects while studying or upskilling.
Enter Data Science Competitions
Kaggle is one of the best platforms to build and share your data science portfolio. You gain hands-on experience with real-life datasets by entering Kaggle competitions. You’ll also learn data cleaning techniques and practice analyzing data and model creation—skills that make you a desirable data science intern.
Post on GitHub
Some hiring managers look at the applicant’s GitHub profile to better understand what they’ve built and how they made it. GitHub is where you can upload your code and interact with data scientists and machine learning engineers. An active account on the platform is a powerful signal that you wish to enter the field and can help you build credibility.
Freelance Work
Another great idea is to pick up side projects. With platforms like Toptal and Upwork, you can sign up as a data science freelancer and work with real-world clients on various tasks. You can then add these projects to your portfolio and discuss them in your application.
It might be initially challenging to land freelance work—especially if you’re at a data science internship level—but when you do, you’ll gain valuable work experience and learn a lot along the way.
How to Build Your Personal Brand to Land a Data Science Internship
Personal branding is your online appearance and what you want hiring managers to know about you. This helps make an even more lasting impression on future employers because they’ll learn what you’re passionate about and what you engage with outside of your application. In other words, personal branding shows more of your personality.
We suggest creating a professional LinkedIn profile. Unlike your resume, this allows you to describe all your projects and work experience with companies in depth.
Employers search for people on LinkedIn often, so your objective as a future data science intern is to show up in the search. For this reason, consider including relevant keywords in your profile.
In addition, LinkedIn helps you gain insights into industry trends or how you compare with other aspiring data scientists. It can also strategically cultivate your network and build your brand.
What to Expect as a Data Science Intern
Data science internships are a unique opportunity for those who wish to gain hands-on experience working with data at a fast-growing company.
Many students and recent graduates have difficulties entering their first job because they’re not used to working with messier and more complex data than they experienced while studying.
Why does that happen? As a student, many of the datasets you encounter are carefully preprocessed by the course instructor—so they’re cleaner than actual real-world ones. This is one of the benefits of taking an internship: to learn how to manage messy data.
Those who have just entered the field have high expectations about the job. Still, it’s improbable that you’ll be tasked with creating a machine learning algorithm immediately because 90% of machine learning is preprocessing, and 10% is modeling.
To summarize, expect messy, raw data and all that comes with this alluring chaos—i.e., the hands-on experience and unparalleled exposure to skilled data scientists that will help you along the way.
As an intern, you’ll be on a team of professionals who solve business problems to create value. We’ve mentioned machine learning, but a more probable workload scenario would involve the following:
- Conducting analyses
- Producing reports
- Building creative data visualizations
- Molding data into a narrative
You’ll be asked to devise metrics, design randomized controlled experiments, and tackle hard, open-ended problems.
This might sound overwhelming for the novice data scientist, but you won’t be alone. You’ll work closely with engineers, product designers, and product managers who will offer support and guide you through the internship.
How to Get a Data Science Internship: Next Steps
The benefits of a data science internship are countless—from working with professionals in the field to building your portfolio and receiving excellent mentorship and networking opportunities. You’ll learn from professional data scientists and demonstrate you’re one step ahead of your peers.
Check out The Ultimate Data Science Career Guide (our all-encompassing resource) for more career guidance and advice on how to break into data science.
And if you’re looking for how to improve your chances of getting a data science internship, the 365 Data Science program offers self-paced courses by prominent industry professionals who leverage their experience to help you break into data science. Begin by mastering the data science fundamentals, build your coding skills in Python and SQL, level up to more advanced machine learning methodologies, and gain invaluable business domain knowledge. Our data scientist career track equips you with the necessary toolkit to confidently pursue your dream job with the right qualifications.
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