How to Get a Career in Machine Learning

As a product manager working with data scientists and on data products, I sometimes get asked for advice about how to get into data science. It's an extremely lucrative field, especially if you know machine learning, the demand for skilled employees is huge and many people seek to capitalise on that. Everyone is hiring!

And yet, you haven't been hired yet. Maybe you are a recent or soon-to-be graduate or someone looking to make a career switch, and you aren't sure how to get that first position that would set you on the right career path. Here's what you need to know:

1. Understand the supply and demand

Even though it's true that everyone is hiring, it's important to understand who they are hiring. At the top of the skill scale, everyone is looking for experienced machine learning scientists and data scientists. At the bottom of the scale, there are lots and lots of people looking for their first Real Job in data, possibly just like you. And this is why so many data job openings stay unfilled for months or years: it's not the lack of interested people, it's the lack of seniority. That means it's important to start getting that experience and building your portfolio of projects, so that eventually you will be in a position where you can choose the best of many available positions.

2. Gather experience, no matter the source

In data, like in everything else, the easiest way to get a job is to already have experience. But since you haven't yet gotten that first job, what options do you have for getting started?

• Formal education: A great way to get up-to-date skills and a thorough, academic perspective into the field. However, it takes time and potentially money, and might require in-between studies if you don't already have a STEM degree.
• Less formal education: Bootcamps and online courses are faster than university degrees although they don't give the same kind of credentials.
• Your current position: If you are currently working as anything related to data, even if it's not actual data science, such as a data analyst, business analyst, business developer, product manager or data engineer, seek out opportunities to learn the right skills and get the right experience. Many organisations have internal training programs to convert current employees into data scientists, as well as hackathons, intrapreneurship programmes and other opportunities for innovation and learning.
• Data as a hobby: Don't limit yourself in thinking that only projects done for your employer count as experience. Of course it's better to have paid working experience no matter which field you are in, but it's also better to have done something rather than nothing. Participate in data hackathons, look up challenges on Kaggle, or look up open data sources and work your magic on them. 

3. Go startup or go corporate

Everyone is hiring for data, small and large companies alike, and each have their own pros and cons. Startups generally have less money, so they might be more willing to hire someone more junior who is hungry to prove themselves and to participate in impactful projects. In a startup, it's also typically easier to try many different things based on what you can do and are interested in learning, and get that precious industry experience. Large organisations would be more likely to hire someone junior for a data analyst position that doesn't immediately give you a touchpoint to machine learning.

On the other hand, larger corporations have more internal opportunities, more people to network with, more data to play with, more budget and more opportunities to work on projects with large, real world impact. Starting as an analyst can be a great way to get close to the right people and the right projects, especially if you don't have enough experience to take on independent responsibility for a machine learning project in a smaller company.

4. Frame everything as an advantage - even your lack of experience

This goes for all job hunting but it's especially true in data: Everything can be framed as an advantage. If you only have corporate experience and you are applying for a startup, explain your familiarity with agile working methods due to your team autonomy within the corporate context. If you only have startup experience and you are applying for a corporate job, explain how you gained a broad understanding of the different business functions but how you now want to focus and work within established processes.

If you don't have much experience, frame that as evidence that you are hungry to prove yourself and get a successful project in your portfolio. Especially the startups will be interested in hiring someone who is junior and therefore cheaper to hire, capable, and much more motivated than someone who has already worked on big projects and might leave soon if the startup isn't exciting enough. Everything is a matter of perspective.

5. Approach companies directly

If a company is looking for people in machine learning positions, even if they are looking for people more senior than you are, it doesn't cost much to approach them and offer your current skillset and enthusiasm. Bonus points if you have done your homework about the company and their industry, so you can already propose ways in which you would be useful.

6. Network, both officially and unofficially

It's a cliché, but not without reason. Opportunities come in the form of people, and especially people who you have given a personal impression to. Big tech hubs like Berlin, San Francisco and London are bursting with meetups either organised, sponsored or attended by companies looking to meet prospective new hires. In meetups, you also have the opportunity to talk to people who have the kind of a job you would like, which also presents an opportunity to find out about working in different companies and about possible open positions.

7. Know that you have something valuable

Data is here to stay, and if you focus on that path and keep seeking opportunities, you will eventually make it. Remember that you have skills that are valuable and sought-after, even if you are at an early stage. You don't even have to do all the things listed in this article in order to succeed; do what feels right for you, keeping in mind that there are other strategies you can try if the first one doesn't work out. And because you are entering a field with a high demand for skilled people, feel free to be yourself and not try to be boring just to avoid putting people off. Quirky hobbies and tattoos are not problem if you are the right person for the job. Good luck!