What Not To Include In Your Data Science Resume
Looking for a data science job has always been an arduous struggle for many. The anxiety of landing a good role notwithstanding, most companies have stringent recruitment processes that involve several rounds of interviews and assignments. But even before any of this, creating a resume that cleverly captures years of education and experience has become critical in order to catch the attention of HR professionals. There are many resources online that instruct people on what they should add to their resumes. This includes presenting relevant skills, learning and practical knowledge accrued over the years through appropriate certifications and projects. But what about the things you should most certainly leave out? In this article, we will focus on the what data scientists should not include in their resumes: Photo In Resume Header While this is often regarded with indifference by most recruiters, some would argue that it looks unprofessional and is even comparable to improper email IDs. Instead, since a header has the potential to create a strong first impression, include information that gives recruiters a window into who you are and details they can actually have some use for. For instance, specifying the level of your expertise (junior/senior) or clearly mentioning that you recently graduated should be included in your resume header. ALSO READ: Common Errors To Avoid During A Technical Data Science Interview Verbose Summary or Objective While the purpose of a resume is to communicate to hiring managers the expertise you hold so they can ascertain if you can be a good fit for the open position, as a rule of thumb, always keep it brief, especially the summary section, which is likely to be one of the first information they will seek in your resume. Since you will be elaborating on your education and professional experience in the following paragraphs, use this space to showcase your background, your area of interest, and for data scientists who transitioned from another field, their passion towards the subject. Irrelevant Skills & Certifications Building on the previous point, condensing your personal and professional experience in a single page requires you to prioritise certain areas over others. How do you do that? By customising your resume based on the job description for the position you are applying for. This means that if you are applying for a more business-focused role or a core managerial position, your certification in marketing that you took during your first job can be left out. The same goes for skill sets that need to be highlighted in the resume – choose to keep the ones that will showcase to the recruiter that you can handle the responsibilities of the job you are being assessed for. ALSO READ: Best Practices For Virtual Data Science Interview Omit Vague Descriptions Of Experience Instead of elaborating on your experience, which you will get a chance to do in the interviews, focus on highlighting your experience through projects that have measurable outcomes. Since quantification is critical in the field of data science, ensure that you exhibit the same in your resume. The same applies to technical skill sets – demonstrate that through real-life experience in the form of projects, instead of simply writing them down. For instance, if you are proficient in Python, prove that by listing projects in which you have used that language instead of bluntly writing that in a bulleted point. Even here, take care to add only those that are relevant to the job you are applying for. Other Things To Keep In Mind Do not add references in your resume, unless it is explicitly asked for. However, do keep a handful of references ready since this will come up at some point during the interview process.While you should not add projects and publications that may not be relevant to the position you are applying for, create a domain where you host them and find a way to add that link in your resume. That way, you can successfully showcase the entire breadth of your experience without cluttering your resume.
Looking for a data science job has always been an arduous struggle for many. The anxiety of landing a good role notwithstanding, most companies have stringent recruitment processes that involve several rounds of interviews and assignments.  But even before any of this, creating a resume that cleverly captures years of education and experience has become […]
Looking for a data science job has always been an arduous struggle for many. The anxiety of landing a good role notwithstanding, most companies have
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Anu is a writer who stews in existential angst and actively seeks what’s broken. Lover of avant-garde films and BoJack Horseman fan theories, she has previously worked for Economic Times. Contact: anu.thomas@analyticsindiamag.com