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Top 6 tips to land your first job in data science

Because of the rapid development of the field and the promising opportunities in the future, many people are interested in entering the world of data science right now. if you have no experience in the field or you are transitioning from a different background, finding your first job in the field of data science can be a daunting task, especially as competition is increasing. This blog gives you six tips to increase your chances of landing your dream job in data science!

1. Learn the necessary skills 

It is vital that you know the skills needed for the specific job role you want to apply for. We already wrote a more in depth blog about the required skills a while ago, so we will keep this section brief. The tools you will use will vary depending on the job, but three programming languages will always come up again and again: Python, R plus SQL for databases. You should be proficient in at least two, better if you know all three of them. If you are not yet an expert that doesn’t necessarily exclude you, but in that case it is important that you show a strong willingness to learn. 

Another requirement for data science roles is a sound understanding of statistics. The most important areas are probabilities, probability distributions, descriptive statistics and statistical inference. Communicating the findings you make using statistical methods is nearly equally important since you will be working with a lot of different stakeholders that probably do not have that same analytical knowledge as you will have it.

2. Improve your CV

Once your skills are at an adequate level to apply for a job, it’s time to present them in an attractive manner. Write a high quality CV! If you wonder how to do that, we can point you to our recent blog post “9 tips to improve your data science resume”. If you put these tips into practice you can be reasonably confident that your CV will be appealing to recruiters looking for talented candidates for a data science role and that you manage to get through the first pre-selection stage.

3. Narrow down your job field of interest 

If you look at the tasks that are advertised in different job ads, you quickly realize that “the data scientist” doesn’t exist since the role description can contain everything from logging external data to AI. This nice visualisation can help you understand how broad the term “data scientist” can be applied.

Most of the companies hire data scientists to do the tasks in the top four layers of the pyramid while the bottom two are mostly done by data engineers.  But there are exceptions, one of which we will explore in detail later on. In order to increase your chances of landing a job it can be very helpful to know which tasks you will want to focus on. Only then you can perfect the skills needed to perform them and filter the job ads accordingly. This will save you a lot of time. It’s probably also a good idea to really stick to the minimum requirements regarding education. If a job ad mentions that a PhD is required, you should probably focus on other opportunities if you don’t have one (even if you might have the skills) in order to save time.

4. Build your area of expertise

Data science is rarely done well only with programming skills and statistics. Most applications of data science require a good understanding of the environment you are working in and it will be hard to land a job in a field that is completely new to you. 

If you hold a degree in an area or have work experience in a field, make use of it! Getting a data science position connected to your area of expertise  will be much easier. And you will be able to create more value with your data skills. An excellent understanding of all the processes involved is often the key to obtain data-driven insights or to develop a strong model.

5. Expand your network 

A thriving network can be very valuable when it comes to finding a suitable job. Go to local data science meetups, join data science learning groups, connect with people in the industry, send a personalized note when you are trying to connect on LinkedIn. All these are possibilities to expand your network and open a door for you. According to a recent survey up to 85% of all job positions are filled via networking! Use this opportunity to connect with other data scientists. You will not only increase your chances of landing a job you like but also learn valuable lessons from people that are inside the industry.

6. Focus on medium and larger companies 

This one might be a bit counterintuitive since data science is a growing field and usually those tend to be filled with startup companies. There are a lot of startups that are looking for data scientists. But there’s a catch. Usually startups are looking for data scientists that are very flexible and can perform all sorts of tasks. Remember the graphic from above? The ideal candidate for a startup spans the whole spectrum, since there are not a lot of people involved (yet). Most startups are looking for people that could qualify as a “Full stack data scientist”. 

Another important point at the start of your career is that you should focus as much as possible on learning. It is much easier to learn if you are in a team with three senior data scientists where every one of them can show you the best practices in his domain. In a startup there are per definition not as many people to learn from. The better strategy for your career could very well be to start at a bigger organization, learn the skills and then use this experience in a startup if you wish to do so. 

Conclusion

Don’t be discouraged by rejections. In the job search process being rejected as a job applicant is the most natural thing there is. This is not personal, you might just not (yet) be the right fit for that time. When it comes to job search, persistence is key (at least once you follow the suggestions mentioned above). By using these tips you can increase your chances of success. We wish you good luck with your applications!