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Have you applied for a data science job but never heard back? (No matter what job you’re applying for — data scientist, data engineer, data analyst, etc.). In fact, a poorly crafted resume or one that includes too many irrelevant details could put you at risk of being rejected.

But creating a good data science resume is a skill that can be learned. Once you know how to expertly polish your resume, you can effectively market yourself when applying for your next data science job.

This article will take you step by step to create a great data science resume.

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1. Structure of data science resumes

· What is the correct length of your resume?

· Create areas of differentiation

2. Add information to your data science resume

· Information priority

· Keep content concise and clear

3. Get feedback from industry experts

4. Create a digital image

The right way to think about a resume is to think of it as a house

This is a very intuitive way to create a resume. Imagine that all houses have a fixed area and a floor plan, and you need to make sure everything fits neatly into the available space.

The same goes for the resume space, which should be used wisely to present personal information effectively.

Keep this analogy in mind as we perform the following steps.

Structure of data science resumes

The first thing to consider is the overall structure of the resume. This helps to plan the sections of your resume and the length of those sections.

What is the correct length for a resume?

One of the most common conundrums when writing a resume is what is the correct length? Ideally, one page is enough. Brevity ensures that the interviewer/recruiter has read what you want them to read.

One page is recommended, but no more than two pages is acceptable. Resumes longer than two pages increase your chances of being rejected. As a rule of thumb, recruiters tend to have slightly too many pages of resumes and should avoid this if possible.

The resume below is three pages long. This is very undesirable and will leave a bad impression on the recruiter or the interviewer:

Take a look at this resume — it’s exactly the template data science recruiters are looking for. A quick scan will give you an idea of your expertise and abilities. Getting information on candidates quickly is a major concern for recruiters, who analyze hundreds of resumes a week.

Try to keep only the relevant information on your resume. For example, if you are applying for a position in NLP, there is not much need to mention that you took accounting in college. Be sure to use your limited space wisely.

But what if one page can’t contain your accomplishments? My advice — don’t hesitate to whittle it down and keep only the details that match the job you’re applying for.

Create a differentiated area

Once you’ve selected the information to display on your resume, it’s time to decide where to put your experience and information on your resume.

Here are a few points to consider when preparing your data science resume:

· Make sure contact details take up as little space as possible and state your current city rather than the full address.

· Your resume should also state your objective, but no more than two or three lines.

· Other things your resume should not leave out:

O Work experience

O project

O education

O skills

O Awards and achievements

· Optional sections can also be included, such as:

O performance in data science hacks

O Contributions to open source projects

O Community participation

O Hobbies and interests

O recommend

· What can be ignored in your resume:

O soft skills

O photos



Add information content to your data science resume

Here comes the core piece of your resume — your data science experience and projects. Likewise, the ability to put everything on one page will come in handy. Here’s how this works.

Information priority

Now you need to think about what to put in each of the sections discussed above. This exercise is crucial because you need to present as much information about yourself as possible and leave out the irrelevant. On top of that, you may even have to sacrifice relevant and important information because of space limitations on your resume.

So prioritizing what is and isn’t on your resume is a key step. It depends not only on your knowledge and work experience, but also on the nature of the position you are applying for.

For example, let’s say you’ve worked on NLP issues before and are applying for a position as an NLP data scientist. However, most of the projects you mention on your resume are related to basic machine learning challenges. This isn’t a good situation for you — the recruiter may be rejecting your resume because they don’t know you can handle NLP tasks.

It’s hard to weed out information. But that’s the price we have to pay when we want the job we want. Review the table below to see what information to include and exclude in different parts of your resume:

In the skills section, you should focus on technical skills or hard skills (not soft skills). Today, almost every job portal screens resumes based on hard skills or keywords related to hard skills.

Similarly, in the “Experience” section, you need to review past projects and select the project or position that is most relevant to the role for which you are applying. If you want to add detail to the project, explain it in a sentence or two or in bullet points. Note: Don’t add any projects that don’t involve data science or analytical problem solving — this has little value from a recruiter’s perspective.

You can also add relevant credentials, blogs (if any), academic achievements or performance in data science competitions.

Now you have the basic template for your resume. So what should we do next? Your resume should be impressive. Read on to see how you can achieve this.

Be concise and clear

To make your resume stand out from the rest of the candidates, combine the following:

· Use active voice, not passive voice. This helps make sentences shorter and easier to read. This will make your resume look more dynamic.

· You quantify the uniqueness and benefits of your accomplishments in each project on your resume. For example, you can illustrate the impact of a project on the business in terms of increased revenue, reduced costs, or return on investment — this is important because it is something that data science professionals often need to do. You have the ability to turn ambiguity into data science — you need your resume to demonstrate industry attitudes.

Get feedback from industry experts


Now that your resume is ready, there’s only one final step left to take — getting practical, experiential feedback on your resume. This is important because when we put our heart and soul into something, we tend to overlook its flaws and drawbacks — that’s a human trait. The only way to correct this is to have your work reviewed by the right people.

At Vidhya analytics, for example, whenever I wrote a blog post about a project, I would ask my teammates to cross-check and comment on it. This has definitely added a new perspective to my thinking, and this feedback will be of great help in further improving the article.

So to build a solid data science resume, it’s important to have industry experts, data scientists, subject matter experts, and so on review it. This requires networking — sharing your resume with people in the industry and listening to their feedback.

You can ask these people specific questions. For example, which three of your five projects should you put on your resume? Or how to quantify a task you accomplished in college or previous organizations.

Create a digital image

So far, we’ve seen the basics of writing a good data science resume. However, in today’s competitive world, having a good resume alone may not be enough to get you called up for an interview — especially if you’re applying for a job as a data scientist.

Your resume should also include digital information.

We are in the middle of a digital revolution. No doubt this will be taken into account in the hiring process.

Let me give you an example. I do some data science interviews every week. Before I make a phone call or enter an interview room, I always look up two things:

· Candidate’s GitHub profile

· His/her LinkedIn profile

I also look at the projects they mention on both platforms — is it relevant to the job you’re applying for? This helps me visualize the candidate’s profile so I can structure my questions in a specific way. I can also tell if the skills a candidate mentions on their resume are reflected in their GitHub profile.

So, to build an impressive and powerful digital archive, follow these tips:

1. Have a good profile on LinkedIn. Most interviewers like to check your LinkedIn profile before an interview (we confirmed this by speaking with multiple recruiters).

2. Create a GitHub persona. Share personal projects and complete code on this platform.

3. Run a blog about data science and share knowledge with the data science community — it helps build a personal brand.

4. Regularly answer data science related questions on Discussion, StackOverflow, Quora and other platforms.

This list is not complete. There are other ways and tools to enhance your digital presence. Keep in mind, however, that this digital profile is created to ensure that it reflects your expertise during your interview.

Creating a profile on every major platform is not viable. Therefore, when creating a digital image, you must be selective.

But be warned — LinkedIn and GitHub are definitely must-build profiles. In addition to those two, you can build your profile on blogs, podcasts, or YouTube (in addition to LinkedIn and GitHub, you should also upload your profile on these sites).




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