Mar 7, 2017

How to get a data science internship

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Your guide to getting a data science internship

Internships are a great way to get your foot in the door at the company you really want to work for. There are plenty of benefits for interns. They get the opportunity to learn from professionals, gain practical experience in their field, and they can build a strong professional network.

Internships don’t happen overnight though. They need time and effort. There are many things you need to work on before you can start applying. You need to ensure that your resume is up-to-date, your cover letter is customized for every company and you are aware of interview etiquette among other things.

If you are in university and wondering how you can get an internship as a data scientist, we have put together some ideas you can use to land that data science internship you’ve always coveted.

The key is to start early. Work on building an online portfolio, research the skills you need as a data scientist and build a network of professionals who will help you. We’ll talk about these and more strategies in depth to help you find a data science internship.

First, don’t worry if you don’t have experience. That’s the reason you are doing an internship – to gain experience in a practical setting. The good thing is that many employers are not hiring for direct experience. They recognize that you won’t have years of experience at this stage. Instead, they are hiring for traits that make people successful in professional settings. Companies know they can train you on the hard skills but they cannot train you on soft skills and grit. Here are some things companies look for when hiring interns:

  1. Attitude: You need to have the right attitude when you walk in to the interview. Employers want someone with a positive, can-do attitude. They want someone who is willing to go above and beyond to get their work done and someone who is adaptable to different situations.
  2. Culture: Every new employee in a company adds a bit to the company culture. This means that employers want to understand the dynamic you’ll bring to their company and if you will be able to complement their existing culture. It’s important that you understand the company’s culture and if it fits how you work. Find answers to questions such as “Is the company fast-paced or laid back?” and “Is the company constantly changing or steady?”
  3. Indirect experience: Yes, we said you don’t need to have direct experience but that doesn’t mean you don’t need to have any experience at all. Your experience in retail working as a cashier counts as experience towards your data science internship. As much as you may only be doing this for some extra cash, employers recognize these jobs as opportunities to learn a variety of skills. They teach you how to communicate professionally, be a leader, negotiate, resolve conflicts and find solutions to problems. These are some soft skills that will help you be successful in your internship. If you don’t have any work experience, start volunteering or working in your community. 

Second, you need to build a professional network. It’s good to start building a network of professionals while you’re in school. These professionals will be able to guide you in the right direction and possibly even refer you at the companies they work in so you can get a data science internship. Here are some things you need to do to build your professional network:

  1. Networking: Look at associations at your university that organize professional networking events. The key is to look beyond your faculty or school and see if there are events with other schools. You can also find events in your city on sites such as Eventbrite and Meetups. Look for data science events, especially if you’re in a major metropolitan area: there should be plenty on those sites. Events taking places outside your university will have a mixed crowd where students, young professionals and managers will be in attendance. Don’t be overwhelmed if you’re in a room full of professionals. This is your opportunity to shine so make the most of it. Make sure you are prepared to talk about yourself, your goals and ambitions. You never know when someone you meet might think you are the right fit for their company and put in a word that gets you at the top of that pile of resumes.
  2. Reach out to your existing network: You need to reach out to people you already know such as your friends, relatives, family friends and friends of friends. Your friend’s parent might work at that company you want to work for but until you advertise yourself and what you’re looking for, people won’t be able to help you. Talk to everyone around you and let them know you are looking for data science internships.
  3. Use LinkedIn to your advantage: LinkedIn will be your best friend while you’re looking for that internship. Get a Premium account if you can (there’s a free trial!) and find people in the companies you really want to work for. Look for Data Scientists and/or Human Resource professionals and send them a message. In your message, give a brief introduction about yourself and your goals and let them know you’re interested in their company and want to be a part of their team. Ask if they can schedule a short call or coffee meeting (if you’re in the same city) to talk about their role and their career path. You will be surprised how many people will be willing to help you out. Send a follow-up message 1-2 weeks later if they haven’t replied to you. Don’t take this as a sign that they don’t want to talk to you – they might be busy and may have forgotten to reply so follow-up is important. If they still don’t reply, message someone else in the company. You’ll be surprised at how responsive people can be when you are asking them for advice!

Third, you need to take advantage of the plethora of resources available to you online and through your university.

  1. Online resources: There are plenty of online communities and resources for data scientists. Check out sites like Hunch, Data Mining Blog, SmartDataCollective, KDNuggets, and Kaggle to learn about what’s going on in the data science community and to stay-up-to-date on the latest trends and skills. There are hundreds of blogs for data scientists in different industries that you can subscribe to for regular updates. You can also take advantage of educational resources such as the data science courses Springboard offers for data scientists.
  2. Offline resources: Every university has a career centre with professionals who can help you with job search strategies, resume and cover letter building and interview prep. Get help from your career advisors on every step of the internship process. Your university may also hold workshops on career different topics so make sure to take advantage of these resources.

Fourth, you need to conduct research to prepare for your data science internship. This is key to getting through any interview process. 

  1. Company research: Make a list of companies you want to work for. This list can be based on the industry you want to work in, the culture you identify with, whether the company has a local office, their brand or leadership. It’s important to know the factors that matter most to you when you’re building this list. Once you have a list of Top 30 companies you want to work for, it’s time to understand what they are looking for and how you can add value to their data science team.
  2. Job descriptions: Start with studying the job descriptions for the companies you have shortlisted. Highlight keywords in every job description and make a list of words and phrases that are common across multiple jobs and companies. Look at the skills and qualifications to understand what companies are looking for. If you don’t have the basic requirements, find out how you can work towards them.

Fifth, build a personal brand to stand out from your peers. You can do this in multiple ways including building a website, writing articles or picking up side projects.

  1. Website/Portfolio: Build a website showcasing your skills and experiences. It doesn’t have to include a lot of information, especially when you’re starting out. Look at it as a live document that you’re going to improve continuously. You can upload your resume here, talk about your journey so far, your goals and what makes you unique. Having a website will give you an edge over your peers. If you’ve done technical work, you’ll want to make sure you highlight the results as well: this article delves into how to build a data science portfolio
  2. Blog: Another way you can build a personal brand is to start contributing your ideas online. You can do this by creating your own blog and writing about your learnings in data science courses at university or by contributing articles to third-party sites. Link your blog and/or articles to your website and LinkedIn to get noticed on multiple platforms.
  3. Pick up side projects: This one may be slightly tougher but is certainly possible. There are plenty of places to pick up side projects that you can add to your resume. Some of these include Toptal and Upwork where you can sign up as a freelancer and work with a variety of companies and startups to gain experience.

Lastly, put together a job search strategy and be prepared to apply to hundreds of jobs. Here are some tips for job search:

  1. Job boards: Many students stick to their university portal to find internships but there are plenty more places to find internships in data science. Some of the best places to look are LinkedIn, Glassdoor, Muse and angel.co. The key is to search on multiple job boards to ensure you are finding all available opportunities.
  2. Company websites: In addition to searching on job boards, don’t forget to look on company websites. Some companies may not be posting every job on external job boards. To make it easier for you, we’ve made a list of companies that are hiring data science interns for 2017. There are plenty more companies but this will give you an idea of the range of industries that need data scientists and the scope of opportunities available to you.

Companies that offer data science internships:

Capital One

Capital One is innovating financial services through a number of initiatives. Data scientists at Capital One are responsible for helping customers solve financial challenges. You will be evaluating tools, integrating internal data with external data sources and designing visualizations to find insights.

Civis Analytics

Civis Analytics offers data science internships in their Data Science Research and Development team where you will be working on predictive analysis, experimental design and algorithm development. This data science internship will be based at their Chicago office where you will work in a collaborative environment while being paired with a mentor allowing you to learn and grow professionally.

Dropbox

Dropbox is looking for PhD level data science interns who will be responsible for security data analysis projects. You will work on designing algorithms and machine learning models to identify security risks.

SAP

SAP’s Innovation Center based in Silicon Valley is looking for data science interns to work on a number of strategic projects. You will be working in interdisciplinary teams to understand machine learning and artificial intelligence.

L’Oreal

This data science internship is for students in Master’s degree programs in data science disciplines. L’Oreal is looking for students who are passionate, creative and enjoy solving problems in innovative ways. This internship will be based in Paris, France.  

Micron

Micron Technology is looking for students for their IT Enterprise Analytics and Data team. Micron focuses on a competitive environment where data science leads the way for business decisions. If you want to work in a place where you will learn, deliver data insights and build innovative solutions, then Micron is for you.

Kayak

Kayak is looking for interns who are passionate about data science and want to help drive business results. You will be working in a fast-paced environment on a number of projects where you will be responsible for processing large volumes of data to find insights.

Nielsen

Nielsen has a Data Science program specifically for data science interns. This program is the Emerging Leaders Internship Program for undergraduates interested in a data science internship with the company. They are looking for future leaders who can work with clients to conduct research, perform qualitative and quantitative analysis and find innovation solutions.

Unilever

Unilever has an R&D Statistician/Data Scientist program for students looking for data science internships. Within this program, you will be applying machine learning, text mining, predictive modelling techniques to gain insights in biological systems and material science.

Oracle

In this data science internship, you will be working for Oracle Data Cloud to implement machine learning. You will be working in cross-functional teams to find the best solutions to target media campaigns.

CIA

The Central Intelligence Agency (CIA) is offering data science internships where you get to create and develop computational algorithms and statistical methods to find insights from the agency’s unique data sets.

AOL

AOL has data science internships in a number of divisions including Alto Mail and R&D. Based on the division you are interested in, your role may vary in terms of the skills and the technical expertise required.

Walmart

Walmart has a digital division called WalmartLabs responsible for the company’s e-commerce business. They are hiring for data science interns to work with data coming from their online and offline e-commerce channels.

Intel

Intel has a variety of data science internships from technical to non-technical. They are looking for experience you have gained in classes, research projects or previous jobs.

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If you want help in your search for a data science internship and personalized career coaching, take a look at Springboard’s mentored Data Science Career Track bootcamp!

Data Science Career Track

Data Science Career Track