They say, the first impression is the last, and thus resumes are the first impression of any professionals including data scientists while searching for a job.
Landing on a data science job is tiring but if successful, can be extremely rewarding as it is the highest paid profession of the current era. With such a great offer, the competition also increases massively with the highest proportion of 33.7% open jobs in data analytics. For getting a potential call for an interview resume is of course crucial. And that’s the reason, having a perfect, updated and relevant resume is critical for landing on a data science job amid this crisis.
While we have spoken extensively about what a data science resume should look like, here, we will discuss some things that one can do away with while writing their resume for a data scientist job.
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Do Not Put A Vague Summary/ Objective
The first thing that data scientists shouldn’t put is a vague and irrelevant summary or objective in their resume. Creating a summary or an objective which is more relevant to the job will provide an extra edge and prevent recruiters from distracting from your profile. As a matter of fact, the whole resume should be completely specific to the requirement of the job; whether it be summary, skills or certifications.
One can start their resume with their specific information as to whether they are junior data science or a senior or whether just a graduate. And then continuing the same, the data scientists can state their objectives and what value they can bring to the business. In short, the more one customises their summary and objective according to role expected, the more chance they have to land on a data science selection pile of recruiters.
Also Read: Tips To Create A Compelling Cover Letter To Land A Data Science Job
Do Not Put Skills & Certifications Which Cannot Be Linked
This pointer highlights the importance of having links in your resume. Traditionally the concept of resume has been a hard copy, however, currently, the requirements have changed, and employers are demanding online resumes. This is where data scientists need to shine. They need to put their skills and certifications with relevant links that can highlight and authenticate that information. The best is to link GitHub, LinkedIn and Kaggle profile on the resume for the recruiters to get the comprehensive idea of the work that has been done by the candidate.
Having these links on the profile will showcase the interest data scientists have for their work and will highlight their practical experience of working on projects if they have no relevant work experience. For data scientists, Kaggle and Github are two platforms that can provide ample information about the quality of work that the candidate has done. These project links will speak for themselves and would help data scientists to validate their skills in front of their potential employers.
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Do Not Just Focus On Method, Show The Result
Another critical aspect for data scientists is the accurate result they can bring out of data, to build better business outcomes. Thus, in resumes data scientists shouldn’t just focus on highlighting the complicated method or algorithm they have deployed for their projects; instead, they should focus on highlighting the accurate results they bring. While the algorithms or the complex methodologies may show your approach to solving a problem, mentioning the outcome of the approach will give you a preference over other candidates.
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Do Not Put Non-Relevant Projects
With the increasing demand of data scientists, the recruiters deal with thousands of resumes in a day, and to stand out among those; data scientists should create one that sticks out in recruiters head. This can be done by including projects and experience which aren’t similar to others and unique to the job that you are applying for.
Data scientists should think of a problem statement in a new way or try out solving different domain problems and place the same in their resume. A resume should speak about their capacity of handling projects, their confidence in solving complex problems, the challenges they have dealt with, and their attitude towards the industry. Thus, add the projects that will highlight your skills, knowledge and capability of solving problems.
Do Not Put Irrelevant Work-Ex
Similar to non-relevant projects, data science candidates should also not add work experiences that do not matter for the data science role the candidate is applying for. Adding irrelevant information in the resume can dilute the required information for the recruiters and makes them lose interest in the resume. This not only damages the reputation but also reduces the chance of landing on a desired interview. For instance, job roles that are not related to data science and were done earlier in the career can be added as just a pointer with designation and company name, skipping the job role altogether. Crisp, precise information is what recruiters look for.
