Data Science is emerging & thousands of new jobs are published daily in the data field. Amid the recession and mass layoffs, jobs in the data science field are not very much affected compared to other domains. If you are planning to prepare for your next interview in data science, you can start by building a perfect data science resume that can not only get you shortlisted but also distinguish you from the other candidates applying for the same job.
This article will discuss the 6 most serious mistakes you should avoid in your data science resume. If you want to know what a data science resume must look like, you can browse through our articles:
Building a perfect data science resume is such a task that sometimes you get irritated when your resume is not shortlisted at the screening. One of the primary questions that arise in almost everyone’s mind is where I should build my resume or what is the best website/tool where I should create my perfect data science resume. In case you are confused about finding the best resource to create your resume, here is our guide:
6 Mistakes to Avoid in Your Data Science Resume
Without taking more time, let us come to the point very straight. Here are the 6 mistakes that you must avoid in your data science resume:
✅Not Putting Github & Kaggle Links
✅Not Customizing Your Resume For Every Application
✅Not Telling the Story of Outcomes in the Data Science Projects
✅Not Inserting Figures & Facts in the Experience Section
✅Not Mentioning How You Can Contribute Differently Than Others
✅Not Highlighting Relevant Keywords & Making Grammar Mistakes
Let us describe each point one by one to get you a better understanding of building a perfect resume for your data science resume.
1. Not Putting Github & Kaggle Links: Going forward to 2023, your resume not only represents your skills & experience but also your portfolio, online presence, and contribution to the data science domain. You must add relevant links to your resume where the recruiters can find the projects you have performed. These links can be your Kaggle project links or a link to your GitHub profile, where they can see the repository of your projects.
2. Not Customizing Your Resume For Every Application: Done by almost 90% of the candidates, this is the most common mistake you make while applying for a data science job. Before applying for a job, read the job description and find the keywords, tools, and technologies that the company is asking for. Customize your resume according to the employer’s needs and mold your resume accordingly.
3. Not Telling the Story of Outcomes in the Data Science Projects: Data Science is all about storytelling. If you are not telling a proper story of the data you have worked on, you might not get shortlisted for the interview. Data Science projects are the best way to showcase your skills & experience in your resume. Most shortlisted candidates have one thing in common: the story they wrote about the projects they have performed. Understandably describe your projects and make sure to mention the overview of the project, tools & technologies used in the project, outcomes of the project, and how those outcomes profitably helped you and your company.
4. Not Inserting Figures & Facts in the Experience Section: Do not only mention your role & responsibilities in the experience section. Companies don’t only want a hardworking employee; they need a curious, visionary, and thoughtful data professional who has an analytical mind and can perform to get them a handsome profit.
Always mention how you helped your past company generate more revenue, how they increased their revenue to 2x, 3x, or 5x with your contributions, how you saved cost & time for your past employer, and how you helped them optimize their business process.
5. Not Mentioning How You Can Contribute Differently Than Others: You can only get a good salary package if your worth is more than your salary package. Mention all your impressive personality traits and how these characteristics can differentiate you from other candidates. If you’re a team leader with good leadership skills, your next employer will need that type of team member.
6. Not Highlighting Relevant Keywords & Making Grammar Mistakes: Mentioning & highlighting the relevant keywords is one of the most essential things in the job-hunting process. Always read the job description that the company provided and mention all the keywords (tools, skills, technologies, etc.) in your data science resume.
Small grammar mistakes can be decisive in the resume shortlisting process. They don’t really matter, but the recruiters prefer you not to make grammar mistakes in your resume. You can use extensions like Grammarly in your browser to avoid grammar mistakes in your data science resume.
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