How to optimize your LinkedIn profile as a data scientist


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As of November 21, 2019, more than 800,000 LinkedIn data science profiles were registered worldwide. Despite this number of available data scientists, it’s no secret that there is still a significant talent shortage. Your LinkedIn profile can have a substantial impact on your career as you search for the perfect data scientist role.

Here are some tips that will help you improve your profile and make it more visible:

1. Creation of a professional profile photo:

There are few things to keep in mind when it comes to adding a profile photo.

You don’t necessarily have to wear a costume, but it is recommended that you choose a photo with an evening outfit.

Then the photo should be sharp and perfectly cropped and it is important that your photo is recent.

2. Title and career summary

LinkedIn allows you to put a description of what you do under your name. It should be a succinct and clear definition of your skills or your job. It should be simple enough that anyone who visits your profile for the first time understands what you are doing.

The career summary goes under your “title”. As you strive to make the recruiter understand what you are currently doing, be sure to include any skills or languages ​​you have learned. This should be very short and to the point for the sole purpose of giving a quick overview of your skills or specialties without the recruiter scrolling to the “Featured Skills and Mentions” section. These details make your profile informative without complicating it. The specialties that you add to act as keywords. These are the words you want people to find. Focus on 3-5 keywords, don’t overdo it and make it boring.

3. Experience

The Experience section is where a lot of people fill in a lot of information while the information you provide here should be precise and clear. Don’t list here all the places you’ve worked for, instead mention the names of companies you’re connected with and can be easily searched on LinkedIn. Usually, when you enter your company name in the “Experience” section, its name and logo should appear.

Be sure to mention the critical roles and projects you have worked for in different companies in bullet form.

When you are a data scientist, it is imperative that this be clear and precise. The reason we put more emphasis on this is that LinkedIn is full of data scientists who may have similar backgrounds and skills. So it might help if your list of experiences is less confusing. In a report by Ryan Swanstorm, there is a crossover of skills between a Data Scientist, a Software Engineer and Data Engineers.

Here are some things you can include in the Experience section:

  • Internships, paid and unpaid.
  • Part-time jobs.
  • Entrepreneurial or independent work.

LinkedIn has a separate section to list your accomplishments like projects and certifications.

4. Profile URL

Create a profile URL. Allow others to quickly identify you in search results by changing or customizing your public profile URL. Simply navigate to your “Profile” and then click on the profile URL that appears in the lower left corner of the window. It should be something like – https://www.linkedin.com/in/xyz-abc-245b5b42 by default. Just click on it, add your name which is easy to read.

5. Competencies and approvals presented

This section is only dedicated to highlighting your strengths. List all your specialties with affirmations from your peers.

A typical list for Data Scientist might look like:

  • C / C ++
  • Python
  • HTML / CSS / JS
  • Java / Android
  • TensorFlow
  • R
  • SQL
  • Keras
  • jQuery
  • Board
  • AWS
  • MATLAB
  • Hadoop
  • Spark

6. List of your achievements:

The Achievement section is to list all your projects, certifications, courses, patents, etc.

For data scientists and other achievements, you can mention your participation in the hackathon. Competitions like those on Kaggle or MachineHack matter more than course certificates. This is especially handy as they are further proof of your skills.

7. Recommendations

See also

Recommendations give you the opportunity to show that you are more than a face on the screen. Pay attention to the recommendations you have from people you have worked with before. This will further describe your portfolio and capabilities. While you’re thinking about including any great recommendations you receive, we suggest that you only mention the ones that showcase your skills.

8. List the areas of interest and create a network

When it comes to generating interest and networking, LinkedIn is a great platform to meet people from similar fields. You are not required to join strictly Data Science groups. Join communities and be active by posting the latest news or trends in data science.

You will often find people sharing their opinion on specialized topics. Use LinkedIn to be conversational, helpful, and come up with ideas.

9. Find recruiters

-Who can reach you?

Once you have configured your profile, there are some settings that you need to enable.

Here’s what your settings should look like:

Allowing others to contact you through InMail is vital as all recruiters contact you through InMail.

– Who can find me?

Your job search preferences should look like this:

Here are some more tips to instill in your profile:

  1. Avoid too many buzzwords.
  2. Your writing on your profile should convey not only your strengths, but also your personality.
  3. Include multimedia in your profile. Use links and upload images to your experience page to enhance the experience there.
  4. Include extracurricular activities, volunteer work, and additional spoken or written languages.
  5. Include your content like articles about your specialization, reactions to industry trends, etc.
  6. Use LinkedIn Data Science groups

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