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by Prof Tim Dodwell

Updated 18 January 2023

Get a Machine Learning Job - How to get a data science internship

How do you land your first job in Machine Learning?
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How do you land your first job in Machine Learning? Understanding the difference between supervised and unsupervised methods is one thing – but the best candidates already have a data science internship under their belt.

At digiLab, we believe that Machine Learning (ML) is a vocation and that you need experience to distinguish good ML from bad ML. So one of the most effective routes to landing a Machine Learning job is to already have a data science internship, either at a company or a university research group! By demonstrating that you can operate with diverse teams, buggy code, and dirty, real world data, you’ll be able to convince “us” (the interviewers) that you know what a real world Machine Learning job “in the wild” is about!

Here are four key strategies (and some small hints) to getting that data science internship experience.

Be Proactive

Being proactive is the subject of the first chapter in "7 Habits of highly effective people". (It’s not a Machine Learning book, but if you haven’t read it, you should!)

This is perhaps an obvious thing to say, but your data science internship won’t find itself: you need to identify opportunities, and compete against other applicants. Try lots of different avenues (see - think beyond “Google” below), be persistent, make up your own AI projects while you wait or take an online course to sharpen your skills!

Soon, you’ll be able to take our online foundation course, “AI in the Wild”, which will teach you everything you need to know to rapidly apply advanced machine learning methods to real world problems.

Ask a University Professor or your wider network

Ok, this is more of a personal one! As a university professor, I am in the role because I believe in and enjoy educating / supervising keen and talented students, and I’m often interested in open contributions to science. Try asking a Prof. “Can I help with your research?” – many will jump at the chance.

Personally, I take any email looking for an internship seriously. Just last week I was approached by an undergrad philosophy student (completely out of my normal group expertise!), who will join me in the summer to explore the design of an “HMI” (Human Machine Interface) for deploying AI in safety critical systems.

70 - 80% of internships are not formally advertised, so reaching out across your network is an effective way to land an opportunity. LinkedIn is a great tool for this. Be sure to personalise your messages and target a particular Prof. / group or company. They will be much more responsive if they can feel the understanding / passion in their particular area.

Think beyond “Google” . . .

Your internship doesn’t have to be at Google, DeepMind, or Amazon to be worthwhile. Although for sure, if you get offered one, you should take it! My former PhD students have, and now some work at these companies on super cool projects. But internships here often have stringent requirements (e.g. currently undertaking a PhD in ML) or are extremely competitive.

You can start smaller: when I was a student at school, I took a less glamorous internship doing data science work for a local hospital. I had to collect data from local doctors' referrals, then build a simple classification algorithm. I ended up building the National Health Service a decision tree (I didn’t know it at the time!).

Interning at a tech start-up is another alternative. Start-ups offer a dynamic, “all-hands-on-deck” environment, enabling you to make a really meaningful contribution to the business and rapidly acquire skills. Why not apply for a data science internship at digiLab?

Some smaller suggestions on how to get that data science internship

  • Consider your timeline. If you are looking for a summer internship, companies will be advertising at the beginning of the year. Make sure you don’t miss the opportunity.

  • Sort out your references. Most companies will require references, so get them lined up beforehand. Asking a member of academic staff or someone in your network, “Please can I use you as a reference for a data science internship?” might lead to opportunities they know about.

  • Get your onl0ine profile polished and up to date. If you have a personal website page or use Linkedin, then make sure it shows the latest information about you. Many companies use Linkedin information in addition to CVs, so ensure everything is consistent and professional.

  • Try a start-up. It might be tempting to go for the big names, but a start-up is an exciting, fast-moving place; you are more likely to get exposure to leadership teams, or have an opportunity to contribute to the front line of activity!

Where to look for a data science internship?

Here are some places where you can look for data science internships:

  • digiLab: At digiLab we offer summer internships in data science, software engineering, ui / ux design and business development. For further details sign up here.

  • LinkedIn: As we mention, lots of opportunities come from your own internal network: it’s easy to apply to internships through your connections or at companies that interest you.

  • Indeed.com: Indeed is known for its job-search platform, but it also posts summer internships.

  • Glassdoor: Glassdoor is perhaps more focused on reviews of potential employees and salary information, but also posts jobs and internships.

  • Handshake: Works with universities directly, so see if your university / college has a link, posts jobs and directly connects you with internships which match your needs and profile.

Prof Tim Dodwell
Co-Founder & CEO at digiLab | Turing AI Fellow | Prof of Machine Learning
Tim co-founded digiLab and leads with enthusiasm and integrity to prototype mathematical solutions at speed to solve industry challenges. Tim has an international reputation in machine learning. Between his digiLab and academic responsibilities, he still finds time to run, time trial on his bike and enjoy his delightful young family.

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