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How To Optimise Your LinkedIn Profile As A Data Scientist

How To Optimise Your LinkedIn Profile As A Data Scientist


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



Here are a few pointers which will help you upgrade your profile and make it more visible:

1. Creating a Professional Profile Picture:

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

You don’t have to necessarily wear a suit, but it is recommended that choose a picture with formal attire.

The next thing is that the picture must be clear and perfectly cropped and it is important that your photo is a recent one. 

2. The Headline And Career Summary

LinkedIn allows you to put a description of what you do under your name. This should be a succinct and clear definition of your skills or job. It should be simple enough to make anyone who is visiting your profile for the first time to understand what it is that you do.

The career summary goes under your “headline”. While you are striving to make the recruiter understand what you are currently doing, be sure to include skills or languages that you have acquired. This should be very short and concise with the sole purpose of giving a quick view about your skills or specialities without having the recruiter scroll down to the ‘Featured Skills & Endorsements’ section. These details make your profile informative without making it messy. The specialities that you add to act as keywords. These are the words that you want people to find with. Focus on 3-5 keywords, don’t go overboard with them and make it boring.

3. Experience

The Experience section is where many people fill a lot of information when the information you provide here must be precise and clear. Do not mention here every place that you have worked for, instead mention the company names that you’re connected with and can be easily looked up on LinkedIn. Mostly, when you type for your company name in the ‘Experience’ section, its name and logo should pop up.

Make sure you mention the roles and critical projects that you have worked under different companies in the form of bullet points.

When you are a data scientist, it is imperative that this is clear and precise. The reason we stress more about this is that LinkedIn is filled with data scientists who may have similar experiences 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, Software Engineer and Data Engineers.

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

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

LinkedIn has a separate section for listing 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 customising your public profile URL. Just go to your ‘Profile’ then click on the profile URL which appears on the bottom-left corner on 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 simple to read.

5. The Featured Skills and Endorsements

This section is solely dedicated to showcasing your strengths. List all your specialism 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
  • Tableau
  • AWS
  • MATLAB
  • Hadoop
  • Spark

6. Listing your Accomplishments:

The Accomplishment section is for listing all your projects, certifications, courses, patents etc,.

For data scientist along with other accomplishments, you can mention your hackathon participations. Competitions like the ones on Kaggle or MachineHack carry greater importance than courses certificates. This specifically comes in handy because these are bigger proof of your skills.

7. Recommendations

See Also

Recommendations give you a chance to showcase the fact that you are more than face on the screen. Pay attention to the recommendations that you have from the people you have worked with before. This will further outline your portfolio and capabilities. While you’re thinking about including every good recommendation you get, we suggest you only to mention those which highlight your skills.

8. Listing Interests and Creating a Network

When it comes to creating interest and networking, LinkedIn is a great platform to meet people from similar fields. You don’t have to join the groups strictly from Data Science. Join communities and be active by posting about the latest news or trends related to Data Science.

You will often find people sharing their opinion on specialist subjects. Use LinkedIn to be conversational, helpful and offer ideas.

9. Finding Recruiters

-Who can reach you?

Once you have set-up your profile, there are some settings that you have to enable.

Here’s how your settings should look like:

Allowing others to contact you through InMail is vital because all the recruiters contact you through it.

-Who can find me?

Your job seeking preferences should be looking like this:

Here are some additional tips to inculcate 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 on your experience page to enhance the experience there.
  4. Include some extracurricular activities, volunteer work and additional spoken or written languages.
  5. Include your content like articles about your specialisation, reactions to industry trends etc.
  6. Make use of the Data Science LinkedIn groups

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