Your resume is the first thing that your prospective data science employer might see to get an idea of the skills that you are adept at. If you are a fresher or are someone shifting careers to data science mid-way, this article on how to build data science resume is the perfect read for you to get your data science jobs.
As the old saying goes, first impressions matter. This is why in this article, we will be discussing myriad ways to perfect your resume for data science roles!
1. Looks matter!
Whilst a data science role typically might be analytical and full of numbers, the data science resume doesn’t always have to be that way. If you are not good at Adobe Photoshop, we recommend you use our resume builder or canva to make sure that your resume looks fair like you.
It is important to remember that employers receive dozens of applications for a role and they cannot practically go through all the resumes at their perusal. This is why you ought to make your data science resume unique and make it stand out on its own.
Here are some tips that we prescribe to build your resume:
- Whilst a resume can theoretically be of any size, we recommend a standard size, preferably 21*29.7 cm (A4 sheet).
- Structure your resume in such a way that you can fit every important info in the appropriate space. It is important for you to remember that your resume has limited space and you ought to make the best use of the space to convey your personal info.
- Keep your resume simple and short. Being crisp and to the point lets you highlight the information that you want your prospective employer to read. Having a resume that is longer than 2 pages is not desirable and might leave a bad impression on your prospective employer.
- Adopt a particular format and highlight aspects of your experience that are related to the role. Let us say, you are applying for a data scientist role that is related to geology and you did a project related to analyzing huge geological data 3 years ago. In that case, you ought to highlight the aforementioned data science certification and projects and give it prominent real estate in your resume.
- Customize your resume before applying to a particular job. Applying for a data science job in startup might require a completely different resume look when compared to applying for an MNC. MNCs prefer a formal resume for the role with minimal designs whilst for startups, it is the opposite. Startups would typically prefer a resume that looks creative and artistic, but there are always exceptions.
This leads us to the next point.
2. Conduct your background research
- Read the job description carefully and mention your key achievements in the form of statistics if possible. A phrase that reads “Increased revenue of the VC firm by 35% with better data models” in a resume is far more effective than a phrase that reads “Highly skilled in analyzing data sets”.
- Match your expectations and objectives with the organization’s expectations. No, we are not telling you to lower your standards. Having a resume that reads something like
“Highly enthusiastic data scientist with 2+ years experience adept at R. Rated yellow on CodeChef. Extremely good at working with small teams”
The following might make you more attractive to prospective employers instead of a generic objective such as:
“A data scientist with 2 years experience looking for roles in startups in Hyderabad/Bangalore”.
3. It is okay to brag
- Yes, you read it right. Especially if you are a data science fresher, it is perfectly ok to brag about your achievements. A very good example of bragging in your resume for a fresher could be:
“Topped the data science ‘class of 50’ at Edugrad.
Contributed to 12 NLP projects in GitHub.
Worked 300+ hours on data science projects in Upwork.“
Your employers providing data science jobs are looking for a reason to hire you. In such a scenario, you shouldn’t be afraid of proving that you are amongst the top-tier if not the very best. Bragging about your achievements in the resume will also not only help you get hired faster but also during salary negotiations.
4. Don’t forget to mention the keywords in your data science resume
- A lot of software companies are increasingly using automated tools to do the initial screening of resumes. This is why the judicious use of keywords is vital. Say, for example, you are applying for a role as a chatbot developer, using keywords such as NLP, Machine Learning, AI, Python, and Tensor Flow are more likely to grab the attention of the person/bot doing the initial screening.
5. Mention your data science certifications
When it comes to showcasing your data science skills, it is key that you mention data science training certifications and relevant skills on your resume.
Be it data science online course or an offline class, attach proof of your data science training certifications and skills so that your employer can verify your skills if needed and for social proof.
6. Mention your online portfolio in resume
In your resume, you should also include some links to external sites where you have done some of your work. This could either be your LinkedIn profile or a GitHub repository or even your personal blog.
If you are an active competitor at Kaggle, you should provide proof of your winnings in the resume if needed.
In conclusion, preparing a resume is only half the job done. You might want to check out our blog here to get some interview tips. At Edugrad, we train freshers and experienced professionals and help them get their dream careers, especially in fields such as data science. Feel free to check us out. And best of luck for your data science interview.
For more reference, you can check out our another blog post on 8 things you should do a day before data science interview
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