Getting a data science job requires you to go through a number of steps and rounds. Even before you send your resume, you’re expected to learn a great deal.

A great resume can help you turn your hours of learning into finally fulfilling the purpose of getting the job. It is important to have an apt resume to get your foot in the door and land an interview.

Here are some mistakes to avoid in your data science resume to increase your chances of being shortlisted.

Mistake 1: You don’t include links

Resumes are not just a piece of paper anymore. Your entire online presence is a testimonial of your work as well. 

Make sure to include links to your LinkedIn, GitHub/ Kaggle, or any other profiles where you have showcased your work.

It makes your work more visible and gives the employer a chance to evaluate it better.

Bonus points if you actively post on these platforms about your work. It gives an edge to your profile and might help you stand out from other applicants.

Many employers actively look for the social media presence of candidates on platforms such as LinkedIn. If you are giving the link to your profile, it is important to have complete and authentic information there.

Mistake 2: Your resume is not specific 

If you’re still sending the same resume to every job you apply to, the chances of being shortlisted get very slim.

Personalization is the key to landing an interview. Try to cater your resume to the specific role you’re applying to.

One way to do this is to look at the job description and note down the top 5 skills that they are looking for. Then highlight those skills in your resume.

One can do this by having a ‘master resume’ with all your experiences and achievements. For each role, choose the 5 experiences that are the most relevant to the job.

You may also talk with some current employees of the company and ask what kind of work they do on a day-to-day basis. Then make sure that your resume reflects that you’re adept with those responsibilities already.

Mistake 3: Your resume is not crisp

As a data scientist, you rarely have any time to be vague. Rather than giving unclear descriptions, try making your sentences short and crisp.

As a thumb rule, use the STAR format for each bullet point. Situation – Task – Action – Result.

Start with what was the problem statement or situation, then the task at hand. Then mention what specific steps you took to work on the situation. Finally, mention the result of your steps.

This ensures that the outcome of your work is highlighted, while also giving a complete description of what was done.

Another key point is to quantify your achievements. Rather than saying you increased the efficiency of a process, you should say that you decreased the time taken by 5% over 2 months.

Mistake 4: Your skills and experiences don’t align

The way you present information in your resume has a huge impact. Rather than telling your employers that you have a skill, your resume should show it.

Rather than telling them that you’re skilled at SQL, show them that you did 2 projects using it. This gives you credibility and a chance to showcase your work.

Mistake 5: Your resume has grammatical errors

This mistake should be obvious enough. As a data scientist, you’re expected to be detail-oriented. So making grammatical errors is almost an unforgivable mistake.

You may have your friends or family proofread your resume for mistakes before you send it.

These are some of the mistakes that you must avoid to make it to the interview round.

As a final piece of advice, make sure you’re comfortable with speaking at length about each point of your resume.

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