Looking for a data science internship in the US but not sure where to start? If you’re an international student without industry connections, the process can feel overwhelming, but you’re not alone.
Internships are one of the best ways to gain real-world experience, build your portfolio, and take the first real step toward a full-time career in tech. Whether you’re aiming for a summer role or a year-round opportunity, this Remitly guide will show you how to find data science internships and stand out in the application process.
You’ll learn how to build in-demand skills, create a strong portfolio, find real openings, and prepare for interviews with confidence.
Build the right skills first
To land a data science internship in the US, you’ll need a strong mix of technical expertise and soft skills that employers value equally.
Core technical skills to learn
Start by learning the basics of programming languages like Python and R, along with SQL for working with databases. You’ll also want to get comfortable using tools like Tableau, Matplotlib, or Power BI to visualize your findings.
A big part of data science involves making predictions, so you may need to familiarize yourself with some machine learning basics like scikit-learn or TensorFlow.
Soft skills that stand out
Data science is not just about building models. It’s also about explaining what they mean. Being able to communicate clearly and tell a story with data can really set you apart.
Employers often value team players. Most data projects involve people from various departments, so being able to collaborate effectively is a significant advantage.
Develop a standout portfolio
If you want your data science student portfolio to stand out, don’t just show off your skills. Show how you solve real problems and tell a story with data.
What to include
Try to cover the full data science process in your projects. Start by showing how you clean messy data and do Exploratory Data Analysis (EDA). Then, include a project where you’ve built a dashboard, maybe using Tableau or an interactive web app, to make your insights easy to understand.
Also, add at least one predictive model, like a regression or classification model, to show that you understand the basics of machine learning.
Where to share your work
If you want hiring managers to easily discover your data science student portfolio, upload your code and project details to GitHub. Then, take it a step further by writing about your work on Medium or Kaggle, or even putting together a simple personal website.
You can also share your dashboards on Tableau Public. These platforms enhance your reach and demonstrate your professionalism.
Project ideas
Not sure where to start? Open datasets, like COVID case data, housing prices, or economic indicators, are ideal for data analytics internships for beginners. They help you practice cleaning data, exploring trends, building dashboards, and even creating simple models.
You could also recreate someone else’s analysis or build a dashboard that helps explain something useful, like predicting home prices or tracking COVID trends.
For a practical starting point, check out the Kaggle housing prices competition. It’s a great hands-on project to build real-world experience. The key is to mix things up, share your work in easy-to-find places, and explain what you did clearly.
Where to apply for data science internships
If you’re not sure where to begin, start your search early, ideally three to six months in advance and use a variety of channels like:
University and career platforms
Your university’s career center and websites like Handshake are great places to look for data science internships. Talk to professors and academic advisors, as they may share exclusive opportunities or recommend you directly to companies that are actively seeking vetted student interns.
Internship job boards
When figuring out where to apply for data science internships, job boards like LinkedIn, Glassdoor, Indeed, WayUp, and Internships.com offer filters to help you narrow your search to roles that fit your skills. You can use search terms like data science intern, analytics intern, or machine learning intern to find internships that match your goals.
Niche and tech-focused sites
If you’re interested in startups or tech-focused roles, try checking out sites like DataScienceJobs, AngelList, and BuiltIn. These platforms cater to candidates with specific domain interests and often feature opportunities that don’t appear on broader job sites.
Diversity and community programs
Career-boosting programs such as Data Science For All, CodePath, WiDS Datathon, and Research Experiences for Undergraduates (REU) offer structured support and networking in the data science field.
These programs often provide mentorship, skill-building workshops, and even pathways to internships, especially for students from underrepresented backgrounds.
Application strategy
Now that you know where to look, let’s explore how to actually land the internship with a winning resume, a tailored cover letter, and smart networking.
How to write a data science resume
Keep your resume clear, focused, and packed with technical depth. Highlight your accomplishments with concise language, using strong verbs and measurable results, like “improved model accuracy by 15%” or “built a Tableau dashboard used by three departments.”
Don’t forget to list the tools you used, such as Python, SQL, or Tableau, and keep sections easy to scan so recruiters can quickly see your value.
Crafting a tailored cover letter
One of the most overlooked data science internship application tips is customizing your cover letter to align with the company’s tech stack and mission. Think of your cover letter as a marketing pitch for your data skills. Analyze the job description and match your experience to their needs.
Mention the specific tools or projects they care about. Start with why the company excites you, then explain how your abilities can solve their data problems. A cover letter written from scratch and tailored for each application ranks much higher than a generic version.
Getting referrals and networking
Networking and referrals can significantly strengthen your applications. Reach out to alumni, LinkedIn connections, or event contacts, especially people at companies you’re interested in. Attend virtual hackathons or data meetups to build relationships with peers and mentors.
When asking for referrals, be polite, specific, and value-driven. Say something like “Hi [Name], I admired your work at [Company]. Would you consider referring me for the intern role? I’d be grateful to share how my [Project] aligns with your team’s goals.” This tailored approach can help you more than blind applications.
Interview preparations
Preparing for your data science internship interview is key to standing out and landing the role.
Technical questions
You’ll probably face technical questions on SQL, Python, and some machine learning basics. For Python, expect questions about loops, list comprehensions, pandas functions, and using libraries like scikit-learn.
SQL questions might cover joins, filters, and how relational databases are structured. For machine learning, you could be asked about model selection, training and testing data, and understanding accuracy and overfitting.
Case studies and business thinking
Beyond coding, be ready for business questions like “How would you improve our user engagements?” or “What metrics would you track for a new feature?” You might also hear questions like “How would you measure the success of a marketing campaign?”
These kinds of questions test your ability to think through real-world problems, use data to find solutions, and show that you understand both numbers and the bigger picture.
Where to practice
Here are some great platforms to sharpen your skills:
- LeetCode for algorithms and Python coding
- StrataScratch for SQL and real-world data science questions
- Mode Analytics to sharpen SQL querying and reporting skills
- Pramp for mock interviews that simulate live data science challenges
By combining coding drills, business-case preparation, and live practice, you’ll walk into your interview calm, confident, and ready to impress.
Advice for international students
Finding an internship in the US requires more than just skills. You’ll also need to understand your visa options, know which companies are open to hiring students from other countries, and make the most of the support from your school.
Understand CPT/OPT rules for internships
If you’re on an F-1 student visa, you’ll need to use Curricular Practical Training (CPT) or Optional Practical Training (OPT) to work legally in the US.
- CPT lets you work while you’re still enrolled in your program, but it has to be directly related to your major and approved by your institution’s Designated School Official (DSO) before you start.
- OPT usually gives you 12 months of work authorization after graduation. And if your degree is in a STEM field, you may be eligible for a 24-month STEM OPT extension if you meet certain requirements.
Search for companies open to sponsorship or practical training
Some startups and tech companies hire CPT or OPT interns, and larger companies often sponsor STEP OPT or even H-1B visas.
You can find these roles by searching job boards like LinkedIn or Indeed using keywords like “CPT” or “OPT.” Don’t underestimate the power of networking; alumni working in the industry can be a great source for insider tips and referrals.
Make the most of university resources
Your school’s international student office is there to help. They can guide you through the CPT, OPT, and international student visa paperwork and answer any questions. Career services can also point you to trusted internship listings and connect you with companies that regularly hire international students.
Breaking into data science as an intern may seem competitive, but the path forward is clear. Success comes from building practical skills, preparing strategically, and staying persistent. From learning Python and SQL to polishing your resume and practicing interviews, each step you take strengthens your foundation and brings you closer to your goal.
If you’re just beginning or refining your approach, take that first step today. Start a new project, update your portfolio, and apply to one internship that excites you. Your data science career starts with an action—start building and applying now.
FAQs
Do I need a master’s degree to land a data science internship?
No, you don’t need a master’s degree to land a data science internship. Many undergraduate students secure internships by demonstrating strong foundational skills in programming, statistics, and machine learning. What really matters is your ability to showcase real-world projects and explain your work clearly.
What should my GitHub profile include for data science?
A standout GitHub profile should feature well-documented projects with clear Jupyter notebooks, including data cleaning steps, Exploratory Data Analysis (EDA) visuals, feature engineering, and model evaluation metrics. Organize your code, use markdown for explanations, and show how your work solves real problems.
Can international students apply for US internships in data science?
Yes, international students on F-1 visas can apply for US internships through Curricular Practical Training (CPT) or Optional Practical Training (OPT). Look for companies familiar with these programs and willing to support STEM students. Your university’s international office can help guide you through the process.
How competitive are data science internships?
Data science internships are highly competitive due to limited positions and a growing number of applicants. However, building a unique, relevant portfolio, practicing for technical interviews, and demonstrating strong communication and problem-solving skills can help you stand out from the crowd.