Getting A Job In Data Science

 

1. Learn The Job Specific Skills

 

When applying for jobs in data science, you have to ensure you do know all skills required by that specific type of activity. We’ve already discussed this in one of our previous articles, so we’ll keep this one short and to the point. The tools you’ll need to be familiar with depend on the job, but most of them require a good knowledge of one of these three programming languages: Python, R plus SQL for databases. This doesn’t mean you won’t be able to get a job unless you’re an expert in all these three tools. However, you should know at least the basics and be willing to learn.

 

A good understanding of statistics is another skill most data science roles require. Descriptive statistics, probabilities, probability distribution and statistical inference are among the most important disciplines you’ll need to be familiar with. As scientists need to be able to share their findings with the world, they need to know how to make use of statistical methods to communicate their progress and breakthrough discoveries to stakeholders and to the general public.

 

1. Build A Better CV

 

Once you have the skills to apply for these jobs, make sure you showcase them in an appealing way. This means you have to write a compelling and attractive CV. If you don’t know how to do that, Harnham UK can help you to put together your CV. Your main goal should be to make your CV attractive to recruiters seeking for candidates for science job openings, in order to get them invite you to an interview. Your CV is the ultimate weapon that can help you get through the initial stage of the recruiting process.

 

1. Narrow Down Your Job Search To Specific Areas

 

Simply taking a closer look at the tasks involved by different roles will help you realize that there’s no actual “data scientist” job as such. These roles may include a wide range of things such as logging external data, artificial intelligence, or development of in-depth analyses and exhaustive reports or dashboards. This chart will help you gain a better understanding of this concept.

 

Most employers seek for data scientists to handle the top four layers of this pyramid, and for data engineers to tackle the bottom two layers. You can increase your chances to get your dream job by focusing on the layer where your best skills are. This will also save you a lot of time, as you’d  only apply to jobs that match you like a glove. Also, make sure you stick to the minimum requirements in terms of education. If you don’t have your PhD, you’ll want to stay away from jobs that list this as one of the must-have requirements.

 

 

1. Build Your Skills And Expertise

 

Programming skills and statistics knowledge are only two of the things data science relies on. In addition, you’ll need to be able to understand the environment you’re working in, as that’s one of the things that could allow you to stand apart from the crowd or job applicants and bring you one step closer to your dream job.

 

If you do hold a degree in a specific area of activity, make sure you put it to good use by applying to jobs that require it. This will make it much easier for you to get your desired data science job. Developing data-driven models and insights requires a very good understanding of all processes involved, hence the desire of most employers to find candidates with matching knowledge, skills and education levels.

 

1. Enhance And Strengthen Your Network

 

A solid network can be priceless when it comes to finding a great job. Attend local data science meetups, join some data science study communities, connect to people in this industry, and try to reach out to some authorities on LinkedIn. Use any opportunity you may find to enhance and broaden your network. According to some analysts, about 85% of all job positions are filled through networking. By intensifying your networking efforts, you’ll increase your chances to get a great job, while also enjoying excellent opportunities to find mentors and to learn from some of the people who are active and reputable members of their scientific communities.