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by Prof Tim Dodwell

Updated 31 January 2023

Get a Machine Learning Job - Tools and Skills You Need to Know

We examine the tools and skills which companies will want to see on your CV/resume
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In article 4/8 of our series, “Getting a Machine Learning Job”, we examine the tools/skills which companies will want to see on your CV/resume and will want to test at interview. You don’t need to be an expert in any, let alone all, of them for your first job or internship – but you must be able to demonstrate familiarity and understanding.

1. Languages

This is a pretty simple and fundamental requirement! You should be proficient in at least one of Python, R, or Julia.

What do we mean by proficient? We mean that you can write code independently and solve relatively vanilla problems. At many companies, there will be a coding test at some stage in the interview process. So don’t say you can do something you can't 😉!

Different companies will prefer different languages. At digiLab, for example, we mostly use Python. But don’t worry if you have the “wrong” language: interviewers are more interested in how you structure your code and think about your algorithms, than in the language per se.

2. Libraries/Packages

If you're applying to become a Machine Learning engineer, you'll be in a stronger position if you can demonstrate familiarity with ML libraries and packages.

Like with languages, every company will have its own preferred stack, some of which you might be able to glean from the job description. But again, don't worry if you've learnt the "wrong" libraries. What's important is that you've demonstrated an ability to understand how libraries/packages work and why they're useful.

But here’s a sample of ones we like at digiLab 😉!

Yes, we’re pro-Python!

3. Maths

Make sure you're sharp on linear algebra, calculus and basic probability (the books we suggest in "Six Top Machine Learning Books" cover these really well). We cover all the practical maths you need to know to get your first ML job in our online course, AI in the Wild: Foundations in Machine Learning. Check it out!

4. Collaborating via Git(Hub)

Maybe you've done some solo-projects to try out new, interesting things. But companies work in teams! Your interviewers will be super impressed if you can point to how you've worked with a team of other engineers on a collaborative project.

A great way to do this is by finding an open-source project which you can make some small contributions to.

This will also force you to use Git (and probably GitHub - that's what we use at digiLab).

For bonus points learn about Continous Integration and Testing. For this, I suggest you start with Git + something like Travis-CI.

Next Steps

This is just a small sample of tools/skills to know if you want to get a data science/machine learning job. But we would emphasise the importance of:

  1. Continual learning - don't just stop with the expected tools. In AI in the Wild: Foundations in Machine Learning we tackle novel ML methods, for example.
  2. Bring something unique to the table! Finding an unconventional package or niche language, demonstrates real curiosity and passion.
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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