Data Scientist LinkedIn Profile Guide & Tips

LinkedIn is a social media platform designed for networking with professionals in your field, establishing your expertise, and connecting companies with employees. Many organizations post their job openings on LinkedIn, making it an important part of your job search. Your LinkedIn profile can act as your virtual resume and include links to your portfolio, endorsements from mentors and coworkers, and other information that signals to recruiters you’re the right person to fill a data science role. The following sections offer tips and best practices to help you make the most of your LinkedIn profile.

How to Make a Data Scientist LinkedIn Profile

When creating a stand-out LinkedIn profile, there are general best practices that everyone can follow. For Data Scientists, there are also additional steps that will help you make the most of LinkedIn as you begin or advance your data science career. You can maximize your LinkedIn portfolio by including the following.

Professional Headshot

Unlike other social media platforms, LinkedIn isn’t the place for your favorite selfie. Your LinkedIn profile picture should convey that you are a professional Digital Scientist. While you don’t have to stick to the standard suite and black-background type of headshot, you should choose either business or business-casual attire. You’ll want a photo that is clear, recent, and cropped in a way that looks good on both desktop and mobile. You may opt to have a professional photographer do your headshot, but this isn’t always necessary. A friend or family member can take the photo for you. Make sure the lighting is good and that it matches the standard displayed on LinkedIn by others in your field, and leave the selfie stick at home.

Personal Statement and Career Summary

Right under your name, you can include a line that describes who you are and what you do. This should be a short phrase that sums up your career and easily explains to visitors what you do. Consider this the “headline” which you can expand on further in the career summary that follows it. Let recruiters know what you currently do, what you are looking for in a future position, and what skills and knowledge you bring to the table. Keep your writing clear and concise. Include your specialties as keywords so recruiters can find you in a LinkedIn search. 

Relevant Experience Section

Some people may feel tempted to include every job and experience in this section, but too much information can overwhelm recruiters, especially if that experience does not relate to your current career aspirations. Tailor your experiences to the type of job you are looking for. This doesn’t mean you are limited to discussing only data science experience or skills. You can discuss skills from your previous professional roles that translate into the role of Data Scientist. For example, Data Scientists need strong communication skills, the ability to work in a team, and a willingness to constantly learn in this ever-evolving field. Use bullet points to break up long paragraphs. You can include internships, full-time work, freelance work, and volunteer work in this section. Hold back on listing your projects and accomplishments as you will mention these in a different section.

Featured Skills and Endorsements

You can use the dedicated section to feature the skills that you are most proud of and that most closely align with your career aspirations. Past coworkers and supervisors, clients, and network connections can also endorse these skills. A typical list of skills for a Data Scientist to add to this section include: 

• Knowledge of programming languages
• Data visualization tools
• Communication skills
• Experience in data collection
• Machine learning skills

To make the most of this section, be specific when listing your skills, such as naming the data visualization tools you have used, or specifying which programming languages you know.

Highlight Your Data Science Accomplishments 

In this section, you can list your data science projects, awards, accomplishments, and certifications. These might include the data science projects found in your portfolio. If you have any industry-recognized certifications, this can go a long way in proving your skills and impressing recruiters. You may also list data science certificates earned. 

Link to Your Digital Data Science Portfolio

Every Data Scientist needs a web-based data science portfolio. This allows recruiters to virtually view your projects and also serves to show your expertise in creating digital reports, data visualizations, and interactive dashboards. You can also use a digital portfolio to showcase your effective communication skills. Include a link to your Data Scientist portfolio in your LinkedIn portfolio so that recruiters can easily find your work.

Data Scientist LinkedIn Tips

Now that you know what to include in your LinkedIn profile, it’s time to leverage your profile to make industry connections, apply for data science jobs, network with professionals, and obtain freelance clients. The following tips will help you make the most of LinkedIn as a Data Scientist.

Create a Profile URL

You want your LinkedIn profile to be easy for recruiters to find. To do this, create a memorable and simple URL. To do this, click on the profile settings for updating your URL, then replace the default text with something simple, most commonly your name. If that URL is already taken, for example Linkedin.com/JohnSmith, you can try hyphenating or adding a middle initial, such as Linkedin.com/JohnASmith, or Linkedin.com/john-smith. You can then add the URL to your contact information on your resume so recruiters can easily connect with you.

Make Sure Recruiters Can Find You

Now that your profile is optimized, you want to make sure recruiters can access it. Check your LinkedIn settings to make sure you are discoverable. Also enable direct messaging on the platform so that recruiters can easily contact you regarding positions you apply for or open positions they believe you may be a good fit for. 

Join LinkedIn Groups

You can find professional LinkedIn groups that offer helpful advice and allow you to network virtually with other data science professionals. You can be an active part of the community by posting helpful content regarding the ever-changing landscape of data science. Share information that is valuable to other data scientists, rather than strictly promoting yourself or your brand. Also, offer feedback to others where you can. This helps to foster an encouraging and beneficial community rather than create a group strictly used to spam other LinkedIn users.

If you do not find a LinkedIn group that matches what you’re looking for, you can also start your own. Doing so allows you to build your personal brand, provide thought leadership, grow your LinkedIn network, appear in more LinkedIn searches, and prove yourself as an expert in data science. Freelance Data Scientists might also use a LinkedIn group as a way of managing clients. Here are some best practices for running a LinkedIn group: 

• Choose a strategic group name that targets the people you want to join. To target a geographical area, add the name of a city or state to your title. You can add other keywords as well, such as a data science specialty. Include keywords that will make your group appear in relevant searches. Group names can’t exceed 100 characters.
• Focus on a niche to create a unique and highly active group.
• Do not make a group a place for spam. Don’t constantly post updates only about your own projects and experiences. Post information that is beneficial to group members. Also, limit the number of self-promoting posts that group members make. 
• Add a custom logo and cover image. Having unique graphics for your group gives your group a professional look and distinguishes it from similar LinkedIn groups.
• Optimize the About section. The About section lets users know the purpose of your group, the type of professionals in it, and the benefits your group can offer.
• Make rules clear and visible. This will help to foster an encouraging and beneficial community. 
• Make sure your group is discoverable. You can’t grow your group long term if no one can find it.
• Invite others to join your group. You can increase your group’s numbers by simply inviting your LinkedIn network to join. 

Use LinkedIn to Search for Jobs

LinkedIn features job listings for available Data Scientist and data science positions. Often, you will use your LinkedIn profile to apply for these positions, which saves you from having to manually reenter information from your profile or resume. You can set up Linkedin job alerts to stay on top of new job listings and be among the first applicants. You may also choose to research the company using its LinkedIn page, and may also look to connect on LinkedIn with the recruiter or Hiring Manager. 

Compare Your LinkedIn Profile to Others

If you want to know how your LinkedIn profile compares to others in your field, locate examples from successful Data Scientists or leaders in the data science space. While you don’t want your profile to look like a copy-and-paste version of someone else’s, this can provide inspiration on how to improve and how to differentiate your own LinkedIn profile. 

Update Your Profile Regularly.

As you gain new skills and accomplishments, update your LinkedIn profile to reflect your growing experience and expertise. Updating your profile while these achievements are fresh in your mind helps to ensure they make it onto your profile. Regular updates also ensure your LinkedIn profile accurately reflects your skills and abilities, so recruiters have a clear idea of everything you can do.

Get Feedback

Having a trusted mentor or professional review your LinkedIn profile provides you with guidance and feedback. Constructive criticism will help you to maximize your profile’s potential, which in turn helps you in finding your next Data Scientist role. Data science certificate programs and courses that include mentoring provide a built-in opportunity for LinkedIn profile reviews. This, along with other job search assistance, can make a remarkable difference in finding a data science job. A mentor can also help you tailor your LinkedIn resume for a data science specialty or for a particular job. You can also receive advice on leveraging LinkedIn to meet your networking and career goals.

Learn the Skills to Become a Data Scientist at Noble Desktop

If you are looking to start a new career in data science, you might think the only way for you to become a Data Scientist is by enrolling in a four-year university or pursuing other costly and lengthy educational options. However, there are many alternative methods available to help you transition into a data science career, including data science bootcamps and certificate courses designed to help working professionals gain the skills needed to obtain an entry-level job as a Data Scientist. Exploring in-person and live online data science bootcamps and certificate programs can help you find the class that meets your career goals, budget, and schedule. The first step to finding the class that fits your needs is to understand the differences between in-person classes and live online classes. 

In-person data science classes meet in a traditional classroom setting at a physical location. In-person classes have the advantage of providing all necessary equipment, such as computers and software, and allowing students to network with local professionals such as your classmates and instructor. You also have the advantage of learning from an expert instructor face-to-face. The primary drawback to in-person courses is the extra time and money required to commute to the physical learning location. Live online data science classes offer many of the same benefits as in-person classes, including the ability to learn in real-time from an expert instructor. You can also collaborate with classmates, and you have the advantage of learning remotely. 

Noble Desktop offers several different in-person and live online data science classes that can help you start a career as a Data Scientist. The Python for Data Science Bootcamp teaches students foundational programming concepts and how to handle different data types, use conditional statements to control the flow of a program, use Scikit-Learn, Matplotlib, Numpy, Pandas, and other Python libraries and tools. Noble’s Data Science Certificate program and Data Analytics Certificate program provide a deep dive into the topics and skills essential to launching a career in data science or data analytics and offer one-on-one mentorship and job search assistance. All Noble Desktop classes provide students with hands-on experience, flexible financing options, setup assistance, a free retake, small class sizes, and real-time guidance from an expert instructor.

Learn more about Noble Desktop’s in-person and live online data science classes.

You can also learn more about data science careers and data science learning options with Noble’s free Data Science Learning Hub.