How to Get a Data Science Internship

A data science internship is one of the most direct paths into the field. According to the National Association of Colleges and Employers (NACE), roughly 72% of interns who complete an in-person program receive a full-time job offer afterward. That’s a strong return. 

Landing an internship without prior experience is the harder part. But the good news is that most early-career candidates are in the same position. A focused approach to building skills and your portfolio is what can help you close the gap. 

This guide breaks down how to get an internship in data science in 7 concrete steps, covering the technical skills 

Key Points 

• Every data science internship is a little different, but interns often work with real datasets and build models.  
• The Bureau of Labor Statistics projects data scientist employment to grow 34% by 2034. 
• You’ll generally need technical skills, like coding languages and statistics fundamentals, to get an internship. The same goes for machine learning and version control. 
• Roughly 36% of job seekers land an interview through a personal connection, which matters more at the internship stage when resumes can look similar on paper. 
• Having a clean portfolio and strong network helps your chances of getting an internship. 

Step 1: Understand What a Data Science Internship Involves 

Data science is among the fastest-growing fields in the US. The Bureau of Labor Statistics (BLS) projects employment to grow 34% by 2034. Internships are among the most direct ways into a competitive data science job market. 

Before applying, it helps to know what the day-to-day actually looks like. A typical data science intern can expect to: 

• Build and debug basic models 
• Create and maintain dashboards or visualizations 
• Document workflows and decisions 
• Present findings to both technical and non-technical audiences 

No 2 data science internships are precisely the same. At a large company like Intuit or Meta, you might work within a specific team on a defined project. At a startup, you might handle several smaller projects across the data stack. 

Not all data science internships are paid. They might last the entirety of a college semester, or they might run their course over a single summer. Companies often look for candidates currently enrolled in or recently graduated from a relevant program. 

Some common internship listings include data analyst intern, machine learning intern, and applied scientist intern. Read job descriptions thoroughly before applying, as the title alone won’t tell you whether the work is what you want. 

Step 2: Build the Core Technical Skills Employers Screen For 

Getting a data science internship with no experience starts with building the core skills employers seek. Specifically, hiring managers are looking for specific evidence that you can do the work.  

According to Gallup, 57% of managers say they’re likely to hire candidates with data science skills in the coming years. Knowing what those skills are is the first filter you’ll need to pass. 

Must-Have Skills  

If you’re working toward becoming a data scientist, prioritize fundamental skills, including: 

• Foundational math (arithmetic, linear algebra) 
• Probability and statistics (distributions, hypothesis testing, A/B testing basics) 
• Data visualization (Matplotlib, Seaborn, Power BI, or Tableau) 
• Managing, cleaning, and interpreting datasets 
• Basic machine learning (ML) concepts 
• Communicating complex technical concepts to non-technical teams  

And, of course, you’ll need proficiency in at least 1 or 2 data-oriented programming languages. Some of the best coding languages for data scientists to learn are: 

• Python for data manipulation, analysis, numerical calculations, and machine learning (pandas, NumPy, scikit-learn) 
• R for statistical computing, graphics, and data analysis 
• SQL for data querying (joins, window functions, aggregations) 

Good-to-Have Skills  

As a data science intern, and especially as you advance in your career, you may want to build your skills in: 

• ML and AI fundamentals (regression, classification, evaluation metrics) 
• Jupyter Notebook 
• Apache Spark for big data 
• Git for version control 
• Cloud platforms (Amazon Web Services (AWS), Azure, Google Cloud Platform (GCP)) 

Quick tip: Pull up a few recent internship listings before you start studying. If a tool or framework keeps appearing, move it up your list. 

Where to Learn  

Formal education works, but it’s not the only route to getting an internship in data science. A few well-regarded free resources: 

• Kaggle Learn: Python, ML, data visualization 
• Fast.ai: ML and deep learning exercises 
• FreeCodeCamp’s data analysis track: Includes options like Python, JavaScript, and Git 

And some paid options worth considering: 

• Coursera’s Data Science specialization 
• DataCamp 
• Data science or coding bootcamps 
• University courses (accredited or audited) 
• EdX (college courses and certificates, free and paid) 

Step 3: Build a Portfolio That Shows You Can Do Real Work 

A strong portfolio goes a long way in landing a data science internship. It signals to recruiters and hiring managers that you can apply data science skills outside a classroom.  

What Makes a Strong Portfolio Project  

Aim for a few well-executed projects rather than a comprehensive log of everything you’ve worked on. Each project should: 

• Use a real, publicly available dataset like Kaggle, data.gov, UCI ML Repository, or a public API 
• Solve a clear question with a measurable outcome, such as a specific accuracy percentage or amount of time saved. 
• Include the full pipeline from data cleaning and exploratory data analysis to visualization and interpretation in plain language. 

Ultimately, your portfolio projects should show potential employers what you can do and how you work through problems and approach data. 

How to Present Your Work  

Host projects on GitHub with a clean README that explains the problem, your approach, and your findings. A short blog post or notebook write-up for each 1 adds context recruiters can read in 2 minutes. 

Aim for a couple of clear, specific projects. If you’re applying for data science internships in different industries, you might want to create separate portfolios with relevant projects in each.  

Above, avoid copy-pasted tutorial projects since recruiters often recognize these on sight. 

Step 4: Find and Target the Right Internship Opportunities 

Knowing where to look and when to apply can be as important as your qualifications.  

Where to Look  

Data science internships are listed in quite a few places. A few sources worth checking regularly include: 

• Company career pages: If you have target employers like Intuit or Amazon, apply directly on their sites. Local companies can be a good source of in-person opportunities. 
• GitHub repo: Volunteer-maintained lists like Summer 2026 Internships aggregate openings across companies and are often updated daily. 
• Job boards: Employers use platforms like Indeed and LinkedIn to find potential candidates. These are good options no matter who you are. If you’re a student, check out Handshake. 
• University career fairs: Your university might have in-person or online events. Meet up with company reps and make a direct impression before even reaching the application stage. You might even get a referral for later on. 

When to Apply  

Big tech and large enterprises (like Amazon) often open their summer internships applications during the fall of the previous year. So, don’t wait until the last minute to start searching. Even if you’re not quite ready yet, applying early helps. 

Some companies, particularly smaller organizations or startups, hire on a rolling basis until they’re filled. If you don’t find an opportunity right now, check back regularly. You never know what might come up. 

Step 5: Network Strategically to Get Past the Application Pile 

Over a third of job seekers (36%) secure an interview through their connections, according to an Express Employment Professionals-Harris Poll survey. So, don’t underestimate the power of networking.  

The connections you make today could very well get you an internship in data science tomorrow. Here’s how to network strategically without it feeling overwhelming or transactional: 

• Connect with data scientists at target companies. LinkedIn is a good place to start. Send a brief connection request with an informal introduction. Once accepted, start engaging with their posts online. 
• Ask for a 15-minute informational chat. Fifteen minutes is often enough. The goal isn’t to ask for a job but to learn how they work and what their team values. People remember candidates who ask thoughtful questions. 
• Be yourself. Reaching out to people without a prior connection can be hard, but try not to overthink it. Being honest and genuine goes a long way with people, even if you don’t phrase things perfectly right at first. 
• Look beyond LinkedIn. Campus clubs are a great place to meet other aspiring data science professionals. The same goes for hackathons and Kaggle competitions. You can also join data science communities on Discord or Slack. 
• Share your work publicly. Even beginner projects invite inbound connections (and potential future opportunities). Use a personal website or LinkedIn to share what you’re working on or your industry insights. 

Step 6: Craft a Resume and Cover Letter That Get Past the Screen

According to Resume Genius, 71% of hiring managers use applicant tracking software (ATS) to screen potential candidates. You’ll need to beat the scan if you want your application seen. 

Here’s how to create a standout resume and cover letter that’ll up your odds of getting a data science internship. 

Resume Essentials for Data Science Internships  

Keep it to 1 page. Include these elements: 

• Skills section near the top: Python, SQL, ML libraries, data visualization tools 
• Summary section: Two or 3 lines that mirror the keywords in the job description 
• Projects section: Two or 3 portfolio projects, each with a 1-line description, tech stack, and a measurable outcome (model accuracy, dataset size, runtime improvement) 
• GitHub link: Either alongside each project or at the top with your contact info 
• Education section: Degree, bootcamps, relevant coursework, workshops 

If you do have experience, include a separate section for that, starting with your most recent experience. Include volunteer or freelance work if relevant. 

Tailoring Each Application  

Generic resumes rarely make it past the ATS scan. For every application: 

• Mirror language from the job description in your skills and summary sections  
• Lead with results, not responsibilities; you might emphasize accuracy, scale, or time saved, for example 
• Keep the formatting clean; heavy design elements can confuse the parser 

In your cover letter, include a brief opener introducing yourself, but avoid generic phrasing like “I’m passionate about data.” Recruiters scroll past them. Instead, start with a brief personal anecdote connecting you to the field. Don’t be afraid to show a little personality. 

Also, dedicate a couple of paragraphs to your projects and experiences that map directly to the role. You might find that having 1 specific, well-explained project outweighs having several. 

Step 7: Prepare for the Data Science Interview Process 

Interview prep is the final stretch, and a focused approach can be more impactful than trying to cover everything. Knowing what’s coming at each stage helps you spend time on what’s most likely to come up. 

What to Expect at Each Stage  

Data science internship interviews typically have a few stages: 

• Recruiter screen (15–30 minutes): You’ll be asked about your background and interest in the role. You may also need to answer basic behavioral and tech questions. 
• Technical screen (45–60 minutes): Most internships have 1 (maybe 2) technical interviews. Expect to answer questions about core data science skills. This can include SQL queries (joins, window functions), Python data manipulation, and basic statistics or ML concept queries. 
• Take-home assignment or case study (4–8 hours): Some companies will ask you to do a take-home project. This might be an end-to-end analysis on a provided dataset. Or it might be an analysis of a company’s growth and performance. The goal is to test you on your applied skills. 
• Behavioral round (30–45 minutes): This interview exists to give hiring managers clarity on how you handle real-world ambiguity. For example, you might need to answer how you would communicate technical findings to non-technical stakeholders. Consider using the STAR method (Situation, Task, Action, Result) to structure your answers. 

How to Prepare  

Interviewers generally don’t expect perfection. They do expect you to know your work and to think clearly out loud. A few approaches to help prepare: 

• Practice SQL on platforms like StrataScratch and DataLemur. 
• Review statistics fundamentals, such as probability, hypothesis testing, and A/B testing. 
• Make sure you can walk through every project in your resume within 5 minutes. 
• Think about what you might have done differently on projects you’ve worked on. 
• Conduct mock interviews with a friend or use Pramp, which is good for behavioral and tech interviews. 
• Practice in the days or weeks leading up to the interview. 
• Think through your processes and possible answers aloud or in front of a mirror. 

Interview stage	Duration	What’s tested	How to prepare
Recruiter screen	15–30 minutes	Your background, role interest, basic behavioral/tech questions	Practice answering questions about yourself and your experience/core tech skills in front of a mirror.
Technical screen	45–60 minutes	Core data science skills (SQL, Python, ML, statistics)	Conduct a mock interview with peers or on Pramp. Practice thinking through your processes (and projects) aloud. Prepare to answer tech-based questions.
Behavioral	30–45 minutes	Conflict resolution, ambiguity, communication with non-technical stakeholders	Use the STAR method: Situation, Task, Action, Results.
Take-home project	4–8 hours	Ability to handle real-world case studies or data science projects	Spend a couple of days (if necessary) to work on the project in a quiet place at home or school.

Start Your Data Science Career at Intuit 

Knowing how to get a data science internship is only the beginning. At Intuit, data science touches more than 100 million customers across TurboTax, QuickBooks, Credit Karma, and Mailchimp. Interns own real projects and work directly with senior data scientists. 

If you’ve worked through the steps above, you’ve already done the harder part. The next step is to explore open data science jobs and entry-level careers at Intuit. 

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