The first step to land a job as a data scientist is the same as in any other profession: create a compelling CV! Although there are more open positions in the field of data science than ever before it is important to have a strong and suitable CV in order to land the job you want. This post gives you nine tips in order to improve your chances to find the data science job you want.
“I increased revenue of our company by conducting data research on our sales data” is a much more compelling way of saying: “I worked as a sales analyst”. If you have prior experience show what you achieved in your role rather than just listing your roles. According to research conducted by the authors of “Brilliant CV: What employers want to see & how to say it”, the applicants with achievement-focused CVs were three times more likely to be shortlisted for job interviews. While resumes in the US are very focused on achievements, it is less common in most European countries. But also here the reader should be able to understand what you did and how it generated value. Possible achievements can include but are not limited to: increased revenue, reduced costs, increased productivity, finished projects and many more.
Depending on the role and position you want to apply to, your CV should highlight different aspects of your career. Try to customize your CV to fit the needs of the company you apply to and to highlight the achievements that fit within the company culture. A customized CV will always have a more personal feel and have better chances of ticking the requirements and being considered for further steps in the application process.
Most data science CV templates you find online do not list all three categories listed in the title. But since the role of a data scientist is filled with tasks that require hard and soft skills it is important to include them. Most people underestimate the importance of soft skills in senior data science roles. In many of our DataCareer Insight interviews soft skills are mentioned as equally important as hard skills. Also make sure to mention your domain expertise. Data science skills itself are great, but you are most valuable to an organization if you understand what they do.
In a quickly changing work environment the ability and willingness to learn new skills quickly is of vital importance. Often it can be more advantageous to signal willingness to learn than to list knowledge of technologies that you barely know. Flexibility and adaptability are characteristics highly valued by any sensible employer. Ways to signal this point are for example by showing private projects on Kaggle/GitHub or outputs of courses - in data science or in any other relevant field.
Learning from people who you admire and hold the positions you want can give you valuable information on how you might want to proceed in your career. Don’t be afraid to ask questions on which skills they might want to see from a potential coworker. And if you don’t have access to people in the position you want, you can read the stories of successful experts in the field of Big Data in our DataCareer Insight series. Try to directly get in touch at events or online through Linkedin. This will give you new insights and potentially open doors!
If you have completed projects in the past show them! Be it a scientific publication, a data science project or just a little script, if you wrote it and the quality of it is good it can provide your future employer with valuable information about your skills and capabilities. Include the links to your GitHub, Kaggle and Google Scholar accounts in order to show your experience rather than just listing it.
Another great way of showing your skills is to have certifications and accreditations on your LinkedIn account. This provides some sort of social proof that you indeed do possess the skills you claim to have. Accreditations from past work colleagues and employers can be especially useful since they hint at the types of work you did in the past.
Don’t make your CV longer than necessary. Usually two pages are the norm. In order to justify more than that you need an extraordinary amount of experience. In the US CVs are usually even shorter, just one page if you are junior. In Europe, you can generally add a bit more information.
If you come from a different background than data science and changed your career accordingly it might be wise to have different CVs at hand for different types of work. Another reason to have different CVs is if you have skills in more than one subgroup of the Big Data jobs (e.g. one for data scientist roles and one for data engineer roles, each highlighting different tools and skills)
Good luck with your applications!
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