Dos and Don'ts of Resume Writing

Many people do not focus on important things while creating resumes and cover letters. We put things in our resume that we believe are important, but this approach is inadequate from a hiring manager's point of view.

Resume

Some important elements of a resume should be addressed with focus because these elements will make your resume outstanding, and the chances of getting selected by hiring managers will increase. Let's discuss each one of them one by one.

Length

The first thing that matters is the length of your resume. It should have a one-page maximum because this is the corporate world, and recruiters get a lot of resumes, but reviewing each of them is a time-consuming task. So, they create criteria to select resumes, and one of the criteria is it should be no longer than one page.

Customize

It would be best if you customized your resume for the company you're applying for based on the job description and responsibilities they have given in the job posting. Try to mention only those projects that are most relevant to their domain. This will also help you reduce your resume length if you've worked on many projects.

Keywords

Keywords play a significant role in selecting you because hiring managers search profiles using keywords to find resumes that match the job qualifications. The thing you should remember is to do your keyword research so that you can put keywords that are more likely to work.

For example, mentioning a project as "Built Recommended System for Movielens" is less likely to work because if you do the keyword research for "Recommended System," you will find that the most used keyword is not "Recommended System" but "Recommender System."

Professional objective

Your professional objective must be data science and the company you are applying for. When you customize your resume, don't stick only to the skills required by the company and the relevant projects they might be interested in. Always mention the company name in the professional objective. This will give them a feeling of interest in the company and the job role you are applying for.

Note: The following is an example of an attractive professional objective. "Experienced freelance data scientist with more than one year of experience in solving complex analytical problems. Seeking to increase data efficiency for Company_Name. Skilled in machine learning, statistics and probability, data visualization, programming, problem solving, and creative thinking.

Certifications

You should add a few certifications because it will positively impact the recruiter. A certified professional is preferred over a noncertified professional during screening if everything else matches the job profile.

Author's note: People can get certifications easily nowadays. However, if two people have the same level of competency but one of them has a relevant certification, then I am likely to hire the person with a relevant certification because I have to choose one of them and I can make my choice using certification as a decision metric.

Freelance or personal projects

Your projects are the key factors that will impress an employer, so you should have them on your resume. Whether they are paid freelance projects, unpaid freelance projects, or personal projects, it doesn't matter. The important thing employer wants to know is whether you worked on projects.

Achievements

It does not matter whether your achievements are small or big. Mention each of them, but they must be relevant to data science.

Data science conferences

Try to attend data science conferences and mention them on your resume. Sometimes it isn't easy to attend conferences because they cost money or only a few selected people are invited. Also, if you get a chance to speak or answer a speaker's question, you will be noticed by the audience, which may help you get opportunities.

Social proof

After receiving your resume, the first thing a recruiter is likely to do is check your social profiles. A recruiter would likely check your GitHub, Kaggle, and LinkedIn profile in the data science industry. So, optimize your profiles on each of these platforms.

Job responsibilities mistakes

It is a widespread mistake by most aspiring data scientists to create a resume that resembles their responsibilities and previous jobs without mentioning how they made a difference to the department they worked in. Here are three tips for writing the job responsibilities that will present you as a professional with experience:

Success measurement

Identify how success is measured for each department where you have previously worked or currently worked. If you are a student, you should consider how grades, conferences, certifications, and projects impact your career.

Present numbers

Once you define the success measurement, you have to represent it, and the best representation method is a numeric value. For example, a software developer might state, "They built 75% of the user interface," and a data analyst might state, "Their report helped the company to invest in four new opportunities." A student might write, "They answered 100 machine learning questions on Quora."

Qualitative success

If your job responsibilities cannot be easily expressed in numbers, then use appropriate language that will define the change you made. It would be best if you used action words to represent qualitative successes. For example, "Resolved client complaints much more quickly," "Motivated colleagues to complete the project," and "Significantly reduced staff turnover."

Negative elements

Nine negative elements will make your resume less attractive, and you already know some of these things, but you might have them on your resume:

• Irrelevant interests or hobbies that do not have any relevance to data science.

• You should avoid unnecessarily stating things you write to fill the spaces. For example, some people mention "references available upon request," "your image," "address," "personal social media accounts," and so on because hiring managers are very experienced, and they will notice it in the blink of an eye.

• Do not provide an academic achievement that is not an area of strength, like GPAs below 3.5 or a percentage less than 70.

• Your professional objective must not point toward what you want to gain from the job. It should be focused on what you can provide.

• Avoid flowery language, such as a statement that uses too many complicated words or phrases to sound skillful.

• Do not include personal information like birthday, gender, religion, or place of birth.

• Do not give reasons for leaving previous jobs and the contact information from previous employers.

• Do not use long paragraphs and phrases that start with an I. For example, "I have completed a predictive modeling project."

• Finally, of course, your resume must not have any grammatical errors. Everyone knows this, but still, we still find resumes with grammatical and spelling mistakes.

Cover letter

A cover letter is a document that provides more information about your professional personality. It should answer the following questions:

• How does your experience meet the job requirements?

• How do your skills meet the job requirements?

• Why do you want to work at the organization?

A recruiter will only look at your cover letter if your resume passes the screening test. Here are four tips from highly experienced recruiters to write an effective letter:

• It should have a memorable introduction like, "Freelance data scientist with four happy clients."

• Do not copy-paste from the internet.

• End with a reason for them to contact you. For example, "I am excited to provide more information about my skills and experiences that can't be explained in this letter; hence I am looking forward to talking to you."

• Stay away from phrases. For example, "To whom it may concern," "Please feel free," "Self-starter," "Forward thinker," etc.