Choosing the right company for a data science internship is an important decision. As a student interested in launching a career in this exciting field, you want to ensure you gain valuable hands-on experience at a reputable organization over the summer months. With data science continuing to drive innovation across industries, there are many great options to consider.
Top Companies for Data Science Internships
When evaluating top companies for data science internships, you’ll want to look for organizations investing heavily in advanced analytics and AI capabilities. Leading technology firms and research-driven companies across sectors like finance, healthcare and e-commerce are leveraging massive datasets to uncover insights and improve products. Here are some of the top places to consider for data science internships:
Company | Key Facts |
---|---|
Tech giant at forefront of machine learning, offers data science internships across different product teams | |
Microsoft | Another top tech firm advancing state-of-the-art AI, provides data science internship within Microsoft Research org |
Social media pioneer uses data science extensively for newsfeed algorithms and ad targeting | |
Apple | Innovator in consumer electronics and services applies data science across hardware and software |
Netflix | Streaming service leverages data science to power recommendation algorithms and content strategies |
Uber | Ridesharing giant relies on data science for routing, pricing and demand forecasting algorithms |
Airbnb | P2P accommodations pioneer uses data science for fraud detection, search rankings and more |
Amazon | E-commerce leader integrates sophisticated data science and machine learning throughout its platforms |
Tesla | Electric automaker advancing self-driving vehicles with help of data science and AI |
Capital One | Major financial institution applies data science for improved credit decisioning and fraud prevention |
As this table illustrates, leading technology companies are at the forefront of leveraging data science. However, major corporations across many industries are also investing heavily in building out advanced analytics and AI capabilities. Wherever you intern, look for opportunities to gain experience in areas like:
- Applying machine learning algorithms
- Building predictive data models
- Performing statistical analysis on big datasets
- Data mining and visualization
- Improving data pipelines and architecture
Key Factors to Consider
When researching companies for a data science internship, keep the following key factors in mind:
Team and Projects
Look for internship listings that provide insight into what team you’ll be working on and what types of projects you’ll take on. Opt for an internship that aligns with your specific interests within data science. For example, if you’re passionate about machine learning, seek projects applying ML algorithms. If you’re excited about data visualization, look for a team focusing on data dashboards and reporting.
Learning Opportunities
Make sure the internship provides meaningful hands-on learning. You want an opportunity to actively build your data science skills, not just observe. Look for chances to collect, clean and analyze real-world datasets under the guidance of experienced data scientists. Apply what you’ve learned in school and expand your toolkit using new data tools.
Mentorship
Strong mentorship vastly improves any internship experience. Look for companies that invest in supporting interns by assigning individual mentors and providing training seminars. You want plenty of networking opportunities with data scientists to learn from. Ideally, you’ll get continuous feedback and advice to help accelerate your learning curve.
Full-time Pipeline
If seeking a full-time job is your end goal, target companies with strong university recruiting pipelines. Learn about their new grad hiring process and data science conversion rates. Some top firms like Google even have programs specifically for interns to network with engineering managers and interview for full-time roles towards the end of the internship.
Application Tips
Data science internships at leading companies are very competitive. Here are some tips to stand out:
- Highlight relevant coursework like machine learning, data mining, statistical modeling and data visualization
- Discuss personal data science projects in your resume and interviews
- Show passion for the company’s mission and how data science contributes to it
- Ask smart, thoughtful questions that show your interest in and understanding of the role
- Ace any coding tests or case interviews that are part of the process
- Get referrals from current employees at the company if possible
Best Locations for Data Science Internships
When considering geography, these locations provide strong ecosystems for data science internships in terms of companies and talent:
City | Key Companies |
---|---|
San Francisco Bay Area | Google, Facebook, Apple, Uber, Airbnb, Netflix, LinkedIn, Square, Strava |
Seattle | Amazon, Microsoft, Expedia, Zillow |
New York City | Facebook, Spotify, JetBlue, Citigroup, Goldman Sachs |
Boston | Wayfair, TripAdvisor, LogMeIn, Rapid7, DataRobot |
Washington DC | Capital One, Mars, NIH, Booz Allen Hamilton |
Chicago | Trustwave, Allstate, United Airlines, DRW |
Austin | HomeAway, Indeed, Dell, Pinterest, Intuit |
Focus your internship search on major hubs where you can work on impactful projects and immerse yourself in a thriving data science community.
Ideal Time for Data Science Internships
Most data science internships align with typical university semester schedules. Here are the ideal windows to apply:
- Summer (May-August) – The most popular time for data science internships. Start applying in January.
- Fall (September-December) – Also a key recruiting season, start applying in July.
- Winter (January-April) – Some opportunities at tech firms and startups, apply in October.
- Spring (February-May) – Typically the smallest internship window, get applications in by December.
If you’re flexible on timing, you’ll have the most options during the summer and fall recruiting cycles. Keep in mind application deadlines at companies often start months in advance.
Top Skills to Develop
To become a strong data science intern candidate, develop skills in:
Category | Key Skills |
---|---|
Programming | Python, R, SQL, C/C++, Java |
Data Analysis / Modeling | Stats, Machine Learning, Deep Learning |
Math | Linear Algebra, Calculus, Probability |
Data Visualization | Tableau, D3.js, Matplotlib |
Big Data | Hadoop, Spark, Hive, AWS |
Software Engineering | Git, Software Design, Testing |
Take relevant coursework and practice using programming languages like Python and R extensively. Learn data modeling techniques like regression and cluster analysis. And highlight any experience with statistics, data mining, visualization and machine learning.
Common Interview Questions
Here are some common data science interview questions to prepare for:
- How would you gather data on [X metric] from users of our product?
- How would you detect fraud in credit card transactions?
- What machine learning algorithms would help classify and filter images?
- How would you validate a predictive model you created?
- Explain how you would handle missing or corrupt data in a dataset.
- How would you determine which features are most important for a model?
- What techniques would you use to reduce overfitting in a model?
Review statistics, machine learning fundamentals and your CS coursework. And practice discussing data science projects you’ve worked on using technical terms correctly. For technical interviews, expect to code – brush up data structures and algorithms.
Conclusion
Major technology companies offer some of the best data science internship opportunities. However, exciting roles exist across sectors in hubs worldwide. Focus on finding hands-on roles with strong mentorship, aligned to your particular interests in data science. By showing passion and preparation, you can get invaluable experience launching a career in this dynamic field.