The Complete Data Science LinkedIn Profile Guide

Why Data Scientists Should Be Using LinkedIn

To date, there are more than 830,000 data science LinkedIn profiles registered worldwide. Despite this number of Data Scientists available/in roles online currently, it’s no secret there is still a major talent shortage. In fact, according to a report by O’Reilly Media, nearly half of all European companies are struggling to fill data science positions. Studies performed by Indeed’s Hiring Lab show an overall increase of 256% in data science job openings since 2013, with an increase of 31% year-over-year as recently as December 2018. Data science is a vast, complex industry with many subsets.

Variations in roles oftentimes require such specific skillsets that positions are left unfilled for an average of up to 45 days. So, what does this mean for you as someone in data science, engineering, or machine learning? You’re a hot commodity. There are start-ups, unicorns, and conglomerates that will want to work with you. We can guarantee it. For recruitment specialists, they want to be able to identify candidates who can offer organisations a unique set of skills. It’s imperative you optimize your skillsets on LinkedIn, and you should start now!

You may also like: 109 Data Science Interview Questions and Answers.

Still Not Convinced?

With over 575 million registered users and more than 260 million of those active on a monthly basis, LinkedIn is undoubtedly the #1 professional networking platform. You have the opportunity to connect with influencers, decision-makers, and leaders within your industry.

The social networking platform bridges the gap between candidates and clients, which has led to more than 75% of professionals now using LinkedIn. This will inform their decision when making a change in their careers.

When a platform, such as LinkedIn, becomes heavily populated with professionals working within the same space, you need to focus on making yourself outshine the competition.

As LinkedIn continues to expand its rate of active users, it’s more important than ever to ensure you’re making the most out of the platform. As J.T. O’Donnell (founder and CEO of Work It Daily) puts it, "a LinkedIn profile is ours to manage. It is no different than Twitter, Instagram or Facebook. It represents us directly."

This data science LinkedIn profile guide will help you create an optimized, appealing profile that headhunters and recruiters on the hunt for your expertise will love.

Why You Should Keep Your LinkedIn Profile up to Date

The benefits of keeping your LinkedIn profile up to date are pretty obvious. You might be wondering however why you would need to keep on top of this if you’re not actively seeking out new opportunities. You might also be wondering why you’re not seeing the results you had hoped for if you’re already using LinkedIn to apply for vacancies.

Whichever category you fit into, you should consider using your profile as the ideal platform to document your role responsibilities, projects, and activities you’ve completed. Why? Because doing so gives others full transparency of your skills and experience which could be exactly what they are looking for. 

Let’s look at this in a bit more detail. Let’s say you’ve described yourself as a team player in your profile description. For the majority of employers, being able to work as part of a team is a mandatory requirement.

Using LinkedIn to document the finer details of your day-to-day duties gives you the chance to prove that you are in fact very capable of working within a team.

Let’s say you’re working as a Data Scientist. Your standard role requirements may look something like this:

• As a lead data strategist, I identify and integrate new datasets that can be leveraged through our product capabilities. I also work closely with the engineering team to strategise and execute the development of data products.

• Execute analytical experiments to help solve various problems, making a true impact across various domains and industries.

• Identify relevant data sources and sets to mine for client business needs and collect large structured/unstructured datasets and variables.

• Devise and utilize algorithms and models to mine big data stores, perform data and error analysis to improve models, and clean and validate data for uniformity and accuracy.

• Analyze data for trends and patterns and interpret data with a clear objective in mind.

• Implement analytical models into production by collaborating with software developers and machine learning engineers.

• Communicate analytic solutions to stakeholders and implement improvements as needed to operational systems.

Sounds Pretty Straightforward Right?

Now, the key here is to think about how many times you have worked on something that falls outside of your Data Scientist role.

We’ve all worked on a task or project at one point to support a colleague or separate department. It doesn’t belong to our core responsibilities, but these moments provide the ideal opportunity to showcase your ability to go the extra mile and work as part of a team to support the business.

So, what does this look like on your profile? Here’s an example from an Associate Director of Data Science:

Your profile immediately becomes more attractive to recruiters when they’re able to get a clear insight into everything that you’ve worked on. This includes achievements within your roles listed on your profile.

Sure, you may have your latest job title included, but having a clear breakdown of your role responsibilities provides recruiters with a better understanding of your skillset and specialisms. This makes you more likely to become headhunted by organisations who are in need of candidates with your expertise!

How To Optimise Your Data Science LinkedIn Profile

Now that we’ve got an understanding of why you should be using LinkedIn, let’s take a look at each individual section of a profile layout. It’s time to make the most out of all that LinkedIn has to offer!

Profile Header

Your profile header is the first thing visitors to your page are going to see. You’d be surprised at just how many profiles do not include any profile or header images. This is an immediate deterrent for recruitment and HR professionals. Not including any images in your profile gives the impression that you are not active on the platform. You will look as though you are not going to be responsive to any communications you might be sent.

Gone are the days of scouring the loft of your parents house to hunt for the family polaroid to get the perfect headshot. Most smartphones, such as the iPhone now have ‘Portrait’ mode, which allow you to take a professional looking profile photos in a matter of seconds. Of course, there are considerations you’ll need think about when taking your head shot photos.

Whilst you may have one of the best pouts the internet has ever seen, LinkedIn is not the place for your weekend selfies or photos of your most precious feline friends.                                                                          

So What Do I Do Next?

There are plenty of resources available online which can teach you how to take professional photos using your phone in no time at all such as this step by step tutorial. Showing your audience who are you with the perfect headshot is the first step to introducing yourself in a more personable manner, encouraging others to get to know you.

In addition to showcasing your best smile, you’ll also want to make sure that you have a header image which preferably relates to your area of expertise. Let’s take a look at an example:

Alyesha Sayle is a Senior Technical Recruiter at Big Cloud recruitment. Alyesha specialises in global recruitment for Data Science, Machine Learning, and Artificial Intelligence organizations. Ideal candidates that Alyesha will be looking to work with will hold specialist skills in Language Technologies, Explainable AI, and People Analytics.

This is a lot of information to convey to somebody who doesn’t already know Alyesha; however, when visiting her LinkedIn profile, we’re able to identify exactly what Alyesha does due to her profile being optimized effectively to communicate her job role and the company she works for.

Now that we know what Alyesha does and the industry she is working in, we’re more likely to want to explore the rest of her profile to develop further understanding of her past and present professional roles.

"About" Section

The "About" section immediately follows your header. This provides the opportunity to explain your role, skills and experience in more detail. You should use this section to feature the highlights that would be included in your CV and are of interest to potential employers.

Consider the "About" section as nothing more than a summary. Remember, following your picture, this is the first part of your profile employers will see so it’s important you get it right. A good or bad bio could mean the difference between receiving valuable opportunities or losing them. Write in first person, not in third. Recruiters often comment on the lack of professionalism that comes across from writing about yourself in the third person. It feels less personable.

Activity

Did you know that your LinkedIn profile displays your recent activity across the entire platform? By default, your four most recent interactions with other users across the whole of LinkedIn will be displayed in your activity feed within your profile.

This activity is visible to anybody who visits your page and it is worth keeping this in mind when you are communicating with other LinkedIn users and organisations.

This is how your recent activity is presented on your profile:

How Is This Beneficial for Recruiters?

Giving your audience insight into what you’re doing on LinkedIn showcases a number of things. For example, visitors of your page can see who you are connected with, which topics you’re discussing, and the industries that you’re tapped into. It is advisable to keep in mind that when posting anywhere across LinkedIn, your comments will be visible for all to see.

When posting content or comments that demonstrate your opinion on a particular topic, it is considered best practice to refrain from engaging in negative activity. This may harm how are you perceived if scouted by recruiters for new career opportunities.

Data Science LinkedIn Experience and Work History

Now, this section is where you’re going to get the bulk of your information laid out in your profile. You have the ability to list each individual role that you’ve worked in. You can include multimedia assets, such as photos, videos, and links that provide evidence of your role.

Whilst you’re filling out this section of your profile, it would make good sense to update your CV at the same time. Having an up-to-date CV will increase your appeal when you apply for jobs using LinkedIn’s EasyApply feature. Not only that, but you’ll also be upload your your cv directly to a new opportunity.

Don’t worry though, we’ve made this part easy by creating the only CV template you’ll ever need which you can download for free.

Most LinkedIn users will include basic information that details their role. They will list the company they work for and the date range in which they’re employed at that company. It’s important not to miss the opportunity here to go into more detail about your role and responsibilities. Expand upon your experience and career development to set yourself apart from the competition.

Nobody wants to read through war and peace when visiting a LinkedIn profile. Keep your descriptions short and to the point, avoiding lengthy sentences and paragraphs.

Remember that LinkedIn is saturated with data science professionals. There will be those that share similar experience and skills as you, so you’ll want to make a conscious effort to keep your profile clear and concise. Be straight to the point and avoid confusing individual areas of your expertise when you list them. A report by Ryan Swanstrom demonstrates the comparison of skills included on LinkedIn profiles for data scientists, software engineers and data engineers.

Of course, there’s always going to be a crossover of skills between certain roles within our industry. However, if your skills are clearly listed within each relevant role with details of how they were implemented, it becomes clearer to the recruiter reviewing your profile where they have previously been applied.

But Why Should I Add all This Information?

It’s worth considering that in your busy schedule, you might have overlooked certain aspects of your work. Some tasks or projects might not fit your job description on paper, but if they support areas of your expertise that an employer will look for, use them! Detailing your past and present roles provide recruiters with the most detailed insight. 

You’re helping them delve deeper into why you are a perfect fit for an exciting new job role. This will increase your likelihood of being contacted, all whilst ensuring you’re selling yourself to the best of your ability.

Treat this section as an opportunity to document your work within each role well. It will give you a place of reference to always look back and reflect on. This can become a great resource at a later stage in your career as you’ll have a record of your personal progression.

How Do I Do This Exactly?

Let’s look at a couple of examples here to give this more context:

Here, we’ll use the example of Big Cloud recruitment’s Social Media Manager.

Example 1: Here, we can see that this individual is working at Big Cloud as a Social Media Manager. The company logo and branding is included, however, there are no insights shared of what this particular role entails. This leaves a lot of guesswork for a recruiter to identify where your strengths and expertise are.

Example 2: Here we can see that this individual works at Big Cloud as a Social Media Manager. They have a break down of what the role entails exactly. Additional contributions are documented within the role description. This demonstrates the completion of projects performed, further supporting the role.

Of course, it might not always be appropriate for you to disclose every little detail about your role depending on your profession. Yet the more information you are able to provide could benefit you in the long run. It will increase your chances of being a successful candidate when applying for roles — and that’s the end goal, right?

Data Science LinkedIn Skills and Endorsements

When it comes to showcasing your strengths, the skills and endorsements section on your LinkedIn profile is of great value. Here, you can highlight your specialisms with reaffirmation from your peers.

For example; If you’re a data science professional, your skills might include some of the following:

• C/C++.
• Python.
• TensorFlow.
• R.
• SQL.
• Keras.
• Tableau.
• AWS.
• MATLAB.
• Hadoop.
• Spark.

Recommendations

Word of mouth still is and always will be one of the most important forms of referral. Strive to have your capabilities, personality and work ethic credited through personal recommendations. See this as a chance to showcase yourself as more than just another face on a screen.

Do pay attention to any references written by your colleagues and superiors. Including recommendations by people you’ve worked with before gives employers live references. This will further outline your professional portfolio and capabilities.

Here’s how your recommendations will appear on your profile:

Written recommendations will only come from individuals you’re connected with. They are only visible on your profile once you have approved them.

Whilst it’s good to have many recommendations on your profile, we’d suggest only approving those that feature your skills. Anything relating to your work ethic or knowledge within your field is great too! Recruiters will use recommendations to verify your ability to perform within a given role.

Listing Data Science LinkedIn Interests

By now you should have a good idea of what happened to the many job applications you made before you updated your LinkedIn profile.

The way we see it, the more effort you put into your profile, the more opportunity you will see from it. You’ve made it this far in setting up your page, but it begs the question; how else can you make use of LinkedIn? This is where interests come in.

Your LinkedIn news feed tailors to you. Not only do you see posts from connections in your network, but also activity from groups and communities you join or follow. Joining groups is a great way to meet new people who share common interests with you. It doesn’t have to be professional! You could share mutual interests when it comes to the daily documentation of Elon Musks ambitious statements for example.

You’ll often find that people are sharing insights and opinions on their specialist subjects. Some people might be asking a question that you have the answer to. Use LinkedIn to be helpful, conversational and offer your opinion.

It’s also worth noting that your interests are visible to your profile visitors. This offers an extra opportunity for your audience to get to know a bit more about you.

I’m Ready, Now Where are all the Data Science Recruiters?

Is it just us, or do you hear crickets chirping whilst waiting for something to happen?

Now that you’ve given your profile a whole new lick of paint, let’s check some basic settings.

Who Can Reach You?

To make sure that you can be contacted by recruiters, you’re going to need to make sure that you have enabled specific settings on your profile.

Here’s how your contact settings should look:

Allowing recruiters to contact you by InMail is vital if you’re hoping to receive new vacancy information. You can be certain that you’re not going to hear from many headhunters if you do not have this enabled.

Who Can Find Me?

If you don’t let recruiters know that you’re looking for new opportunities, you’re not going to hear from.

Your LinkedIn job seeking preferences should look like this:

What’s Important in Data Science, Machine Learning, and Artificial Intelligence Job Applications?

Now, we’ve covered the areas in which you can make the most out of your LinkedIn profile. Let’s drill down into the data science recruitment process. The Big Cloud recruitment team collectively hold more than 20+ years of experience in the talent search. Specifically, professionals working within Data Science, Machine Learning, and Artificial Intelligence organisations.

We sat down with the recruitment team to gain more insight into how you can avoid common mistakes frequented on data science profiles. Taken from a headhunters perspective, we take a look at what information is the most appealing when a job application is being reviewed.

Q. What are the most common mistakes you see on a data scientists profile?

A. Buzzword Bingo. Only list skills that you can clearly show you have experience in. Better still, support your skillset within the context of a project description. If there’s no evidence that you have applied your experience to any given project, it implies you’re not qualified for the role you’re applying for. You’d be surprised how many applications I receive from Data Scientists that do not include any references to projects that they have worked on — Alyesha Sayle: Senior Technical Recruiter (Language Technologies, Explainable AI & People Analytics).

A. Having lots of text in your profile summary is off-putting and unnecessary. I’d rather see a profile description that has a concise overview of key work objectives and experience. Also, candidates who don’t accept InMails are making a big mistake. If you’re open to new opportunities but aren’t accepting InMails, you could be missing out on new, exciting roles. Take the time to check you have your settings optimised so that you can be contacted by a recruiter. — Tom Harris: Principal Consultant (Data Science, Machine Learning, Deep Learning, Computer Vision & Artificial Intelligence).

Q. How quickly can you tell if a data science candidate is a strong fit for the role?

A. It becomes apparent within the first 5 - 10 seconds of browsing an applicants profile. A strong candidate will have taken time to perfect their profile and communicate their professional experience. It’s refreshing when candidates have distinctly injected some personality into their profile. This near enough always encourages me to keep reading through their information. It’s also a huge bonus if the candidate has listed examples of projects that they have worked on. It means I can direct them to more relevant and exciting roles and nobody's time is wasted. — Nicholas Jackson: Data Science Recruiter (Machine Learning, Deep Learning, Computer Vision & Artificial Intelligence).

Q. How much information do you expect to see for each role listed on a LinkedIn profile?

A. For me, I just need 3 – 4 sentences on each role that a candidate has held with a clear list of the tech they’ve worked on, whether that be in the profile summary or listed in keywords. If there is no information listed under each role, I’ll most likely move onto the next applicant — Chris Harrison: Senior Consultant (Data Science, Machine Learning, Artificial Intelligence, Deep Learning, NLP & Big Data).

A. The more information available from an applicant, the better. The less information you have available on your profile, the lower the chances of your account showing as fitting for a potential new role. This means you could be a great candidate but might not be contacted because your profile doesn’t reflect who you are and what you can do. — Chris Bradbury: (Deep Learning & Computer Vision Recruiter).

Q. Are there any specific skills that you look for that makes a candidate more likely to be successful?

A. When it comes to working within data science, most organisations are looking for candidates with a core set of industry-standard skills. There are too many candidates out there applying for jobs that fail to either possess or include key skills, such as Python, Data Science, Machine Learning, and AWS. If you have these included in your profile, it makes a big difference. Also, it’s never nice to read somebody talking about themselves in the third person — Dan Kettle: Senior Technical Recruiter (Data Science, Machine Learning & Artificial Intelligence)

Q.  What would you recommend to a data science LinkedIn candidate who wanted to stand out when applying for a job?

A. One thing that always stands out to me is when a candidate has included a link to either their own website or Github profile. Better yet, include both if you have both. Being able to dive right into an applicants background and ability always stands out to me when I’m reviewing applications and I’ll be more inclined to contact candidates who have considered including this information — Jess Bergin: Senior Technical Candidate Recruiter (Machine Learning, NLP & Conversational AI).

Q.  Are publications written by Data Scientists on LinkedIn important to you when you’re reviewing a job application?

A. Most certainly yes. It’s quite common for candidates to include details of their publications on their CV and if I am contacting a candidate after reviewing their application, I’ll often ask for details of where this can be found. I think it would be hugely beneficial for data science applicants to make use of LinkedIn’s article writing as it’s great for raising awareness of who you are and gives recruiters more insight into your experience when browsing your profile. — Carsten Jahn: Recruiting AI Experts (ML/DL, CV, NLP & Robo)

Big Cloud’s Top Data Science LinkedIn Tips

• Avoid using technical jargon and buzzwords within your profile writing. Keep all of your writing as human as possible. Your goal is to communicate your expertise and individuality whilst maintaining a clear and concise description of your professional profile.

• Inject your personality into your writing. Your aim is to convey not only your strengths and abilities but also who you are as an individual. Remember to keep it professional and refrain from sharing negative views which could harm your chances in being approached by a new employer.

• Include multimedia within your profile. Each entry within your experience section enables you to provide links to websites and the ability to upload images to enhance the view of your entries. Make the most out of these features to assist in making your profile visually arresting.

• Include extracurricular activities, volunteer experience and additional spoken/written languages.

• Create your own content – LinkedIn enables you to upload documents. Showcase your expertise by writing articles surrounding your specialist subject, reactions to industry trends and events in support of generating a following amongst your industry. If you’ve already published articles related to Machine Learning, Deep Learning, Artificial Intelligence & Data Science, repurpose these and upload them to your LinkedIn profile to support the growth of your network.

• Make use of data science LinkedIn groups. Using groups will allow you to join communities and make connections with others who relate to your areas of interest. Engaging in group activity will lead to new ways in which you can be discovered and considered for new job opportunities.

• List your achievements and who it is that you want to connect with. Use every line within your profile description to sell what it is that you do and what you can offer.

 Further Reading

• What Data Analysis Tools Should I Learn to Start a Career as a Data Analyst?
• Comparison of Data Analysis Tools: Excel, R, Python, and BI Tools.