It’s no secret that having relevant work experience is often crucial to get hired in a technical field such as Data Science. That makes it difficult to start your career if you don’t have any experience to begin with. You can easily end up in a vicious cycle where you need to have experience to get experience, regardless if you are coming straight out of academia or switching careers.

In addition, few companies take a chance on a recruit without a proven track record and may only consider candidates that have experience in their particular industry. If they do offer opportunities to juniors, they are most likely choosing candidates that already have numerous relevant internships, volunteering experiences and side-gigs.

So how can you start building credibility with these employers in order to land your dream Data Science Job?

Having interviewed 50+ candidates, across 6 years working in technology consulting and Data Science, I can give you the perspective of the employer. It is no longer enough to have a solid academic background in a quantitative field to get these types of opportunities. In order to get hired, you need to stand out as the top candidate.

I don't mean to scare, but graduates often underestimate how difficult a job search can be. It is anyway better to be over-prepared than risk not getting the job you want.

So, in order to help you stand out and get a first crucial step in the door, here is a list of strategies you can use to rocket-launch your career into Data Science.

Make sure your technical skills are on point

Of course, the single most important part of landing your first Data Science job is having the prerequisite training. In particular, you need the appropriate basis of engineering and statistical studies, otherwise you won't get very far as a Data Scientist.

Most applicants that get into Data Science do so via a quantitative study such as a Bachelors/Masters degree in a STEM field. Nowadays universities also directly offer Data Scientist programmes.

That being said, many senior Data Scientists have had unconventional paths into Data Science as the field is young. Common to all is a strong interest and a solid base in statistical analysis. It is also not impossible to get hired if you lack a degree, if you make up for it in experience or with other types of training.

In case you need to brush up your skills, then these courses will quickly bring you up to par.

Classic Machine Learning course by Andrew Ng on Coursera.

Cover the basics with the Data Analyst Nanodegree on Udacity.

When it comes to which programming languages you should learn, such as “R vs Python”, it depends on what type of work you want to do. R is suitable for analyses and insights. Python is suitable for re-usable data pipelines and building software products. I lean more towards Python as the world is going towards more automation. Many discussions can be found online with for example comparisons on StackOverflow and in-depth blog posts.

Build an online portfolio and contribute to open source

You can easily get the attention of recruiters and future colleagues if you have an active GitHub profile. Contributing to the open source community is one of the strongest signals of technical skill there is, especially if you have a popular repository.

If you can't find any interesting open source projects to work on, you could perform your own data analysis on a topic that interests you. There is plenty of financial data, web data, text data and other types of online data readily available, begging to be scraped and analysed. If you make a modular design out of your analysis, the open source community can re-use your code as well.

See for example a St. Louis County Segregation Analysis, with an accompanying article. Another example is a notebook on Analyzing San Francisco restaurants using Open Data. Structuring your project like this could get you a top repository without much effort.

After your analysis you can publish the result in a blog post. This will contribute to your online presence and will extend far beyond your next employer - your name could be associated with high quality content throughout your entire career.

Other ways of showing off your machine learning prowess is for example participating in Kaggle competitions or answering questions on StackOverflow.

Network at meetups and company events

One of the best ways to get to know employers better is to participate in their community events and meetups. At meetups you get to have conversations with Data Scientists that are already working where you want to get employed. This way you can get a feel for the team vibe, team technology and office environment.

If you get to know these Data Scientists better, it’s easy to get a referral which they often get a bonus for. If you have made a good impression it's also easy to get tips and advice for the interview process.

Make sure to keep attending or even hosting meetups once you’re employed. This way you can easily get leads for new projects and continue building on your reputation within the local community. You would be surprised how many projects come your way just by having a chat with someone facing similar problems that you have had.

Crush your technical assignment

Another prominent stage of many technical interview processes is the technical assignment. If there is any single most important moment to make a good technical impression - this is it! There is no greater equalizer than kicking ass at a technical assignment as it signals many things to prospective employers

The way you plan your work

Your technical skills

How well you comment and structure your code

Your delivery style

The way you communicate and present your work

And so on. If you suspect you have made a weak impression in other stages of your interview process, this is your moment to shine. You can do this by

Working diligently to create high quality deliverables

Ask clarifying questions to whoever gave you the assignment

Get feedback from your technical friends on your delivery

Rehearse any presentations that you made if they are a part of the case

And finally, deliver on time (or even a day or two before deadline!)

Personally I find this stage the most important as it gives a lot of insight into what it will be like to work with the candidate. At earlier stages candidates may be nervous, have a bad day, or sadly even get discriminated by biased interviewers.

By putting effort into the delivery of a case, you remove many factors that could be due to “bad luck" as you can work on your case in peace. This is typically how you will work when you are later employed anyway.

Make a ridiculously good impression.

With all these tips in mind we can sum up - make a kick-ass impression.

The impression you make should be so strong that your favourite employer simply has no option but to hire you before another employer has a chance to.

That’s it, to start.

With that being said, I hope these tips have helped you organize your thoughts and application process.

If you enjoy this sort of content, make sure to follow me for part II where I dive into further topics such as internships, soft skills, whiteboard coding and interview practice.

Best of luck with your search!

Hakim

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Special thanks to Marco, Teo and Ida for feedback