LinkedIn Recruiter Secrets for Effective Sourcing


LinkedIn is one of the most popular and powerful platforms for recruiters to find and connect with potential candidates. However, with over 800 million members, navigating and filtering through the vast amount of data can also be challenging. How can recruiters optimize their sourcing process on LinkedIn and find the best talent for their roles?

That’s the question that Irina Shamaeva, a renowned sourcer, and partner at Brain Gain Recruiting, addressed in her recent presentation on advanced sourcing techniques on LinkedIn. Irina is a recognized expert in the field of sourcing and sourcing trainer, with over 20 years of experience and numerous awards and publications. In her presentation, she shared her insights and tips on how to leverage LinkedIn’s features and data to improve the quality and efficiency of sourcing.

Split into two parts—”What You See is Not What You Get” and “Advanced Techniques and Insights”—her session dives into the platform’s complexities. It tackles common challenges like navigating the interface, understanding search algorithms, and making the most of filters. 

Let’s dive in!

Two Approaches to Sourcing on LinkedIn: Targeted and Open-Ended

LinkedIn Recruiter’s documentation is lacking, and there are some undocumented tricks. It’s a process. You take different steps toward your goals. 

The first approach is targeted. You start with a job description and search for everything: must-haves, nice-to-haves. Often, you won’t find anyone and you’ll have to relax your search. You can add ORs for similar titles and skills to find more. You’ll quickly find matching results. But everyone searches like this, so your competition will find them too. And you might miss profiles with slightly different wording.

The second approach is open-ended. (This is how Irina usually searches.) You start with minimal criteria and use match-health filters. Then, you’ll see profiles that don’t match, and you start excluding those words using the NOT operator. The advantage is that you’ll find members who might not match because you’re searching more inclusively. It’s important to understand that you won’t find everyone with the right search. You’ll need to keep looking at the results, refining your search, and learning from what you see. You might learn what to exclude or what terms to pair together. For example, Java goes with the backend.

Here are a few examples.

If you search on Google, the OR operator works against you. Google is better at understanding words and varying them as needed. LinkedIn needs to be told exactly. So this search provides four different options to look for account executives or sales managers. 

Another example: searching for pediatric doctors and primary doctors, varying the words. 

Another example: searching for human resources business partners, again, varying the words. 

And here is Irina, searching in a very open way and seeing nurses and assistants while looking for a doctor. She starts excluding those words and gets a wider list of results.

Another example: while searching for an internship for staff and adding some keywords, but managers and recruiters pop up. You can exclude them.

Irina recommends using both approaches in combination, depending on your needs and goals. For example, you can start with a targeted search to find some candidates that match your requirements, then switch to an open-ended search to expand your pool and find more diverse candidates.

The Hidden Search Operators in LinkedIn Recruiter

The next segment delves into the realm of hidden search operators within LinkedIn Recruiter. These operators were never officially documented, although they once functioned on However, that’s no longer the case. A select few, including Irina and some others, have uncovered these operators through trial and error, discovering what works and what keywords to use. Irina is eager to share this list of operators that function in LinkedIn Recruiter, LinkedIn, and Recruiter Lite, though unfortunately not in Sales Navigator.

The format for these operators is crucial; they must be placed either in the job title or company fields to communicate effectively with LinkedIn Recruiter.

The full list of these operators is invaluable, as they are all currently functional, with some of them requiring specific codes. This list can be found on Irina’s blog. These operators are keywords that you can use in your search query to filter your results by specific fields or attributes.

Let’s take a look at a couple of these operators:

  • headline: This operator lets you search by the headline of a profile, which is the short description that appears under the name. People often include crucial information in these sections, such as skills, spoken languages, degrees, and seniority. For example, you can use the headline:”software engineer” to find profiles with software engineer in their headline. 

There’s no official way to search the headline, and headlines can say Open to world hiring.

  • skills: A LinkedIn recruiter has changed its skills search to be almost like keywords. And with the operator skills, you search by the skills listed on a profile themselves. For example, you can use skills:”python” to find profiles with Python as one of their skills. 

See some examples of how to apply these two operators in the image below. 

By understanding and utilizing these operators, recruiters can conduct more targeted and effective searches, ultimately leading to better matches and more successful recruitment efforts.

These operators not only enable the selection of specific values but also the exclusion of others. 

Irina advises using these operators with caution, as they might not always return accurate results. For instance, some profiles might not have updated their headline or skills or used different terms or spellings for their company name. Therefore, it’s important to cross-check your results with other sources and verify the information before reaching out to candidates.

The Concept of a Real-Time Aggregation Solution

In the next part, we’ll explore what’s called the “real-time aggregation solution.” This method starts outside of LinkedIn, looking for potential candidates in different places. Then we collect the data there and then go to LinkedIn, cross-referencing the data from the previous step. Then we find the contact information, and we’re ready to get in touch with the potential candidates.

This approach is fresher and more effective than just using LinkedIn alone because it combines information from multiple sources. It’s about casting the net wider, tapping into varied sources, and then seamlessly merging that data with LinkedIn insights for a fresh and dynamic approach to candidate engagement.

The key takeaway here is the synergy gained from leveraging multiple sources, which is way better than relying solely on one platform. Basically, we are creating a mini-people aggregator with the data collected exactly for our purposes.

Let’s go through it step by step: 

1. How do we find non-Linkedin sources? 

If you’re wondering where else to look besides LinkedIn, Google for specific sites like for designers or GitHub for software developers and other tech enthusiasts. 

With the help of Google you can also find contact lists in Excel (for example, with operators like filetype: or ext:)

Example: filetype:xlsx “list of” site:* (add needed context to the keywords in quotes)

Other resources like healthcare license verification sites can also prove to be invaluable, as they contain the names of licensed individuals.

Or let’s say you have a specific person profile as an ideal candidate. So you can search for an ideal candidate’s name, username, or email. This can lead you to pages where similar profiles can be found. These could be lists or membership of certain groups. 

And then on networking sites like LinkedIn or Facebook, you can find networking groups and take a look at lists of their members. 

2. What do you collect? 

Getting email addresses is important because they’re like a ticket to contacting someone. Even if you only have a LinkedIn URL or someone’s name, it can still help you find them. Plus, data from other sources (usernames, first or last names, etc) can add extra info to help you craft better messages. 

As you gather data from various sources, you’ll find that it may contain additional information not available on LinkedIn, which can aid in crafting personalized messages. For instance, you can mention where the first reference is coming from, such as a GitHub profile, group member, or licensed nurse.

3. Proceed to LinkedIn with the information collected

You locate these people on LinkedIn, and you narrow it to a target audience because now you have information from LinkedIn (job titles, companies, etc), that can help you narrow it down. You enrich it with context, and then you can reach out to those people via multiple channels. 

4. A cool trick is combining data from GitHub with LinkedIn

GitHub is a platform where developers collaborate and write code. However, not all code writers are software developers. Some could be students, professors, retirees, or coding enthusiasts. GitHub profiles (unlike Linkedin) do not have job titles, so we don’t always know who these people are. We may know that they excel in a specific programming language and live in a certain place, but LinkedIn can provide more information about who they are.

Then there’s a handy tool for finding GitHub profiles for tech recruiters and sourcers, which makes it easy to gather info like email addresses. You can then download this data in CSV or Excel. And import into LinkedIn for more targeted outreach. This tool is excellent at finding email addresses and does not even require a login at the moment.

So this is what it looks like. A typical search would be searching for a programming language, searching for a location, and there is also a sorting operator. Here the results are sorted by the number of followers. 

5. Create a new project

Now you have an Excel file with that information exported from the GitHub tool. And you go to a LinkedIn Recruiter project and create a new project. It shows you that you can download a sample file that has some fields, but you can get away with the simplest template. The template should have the correct emails and should have first and last names, but they can be just about anything.

6. Get a good chunk of profiles

After you have imported this file, profiles that have been identified by email are in your project, and profiles that did not find a match by email are not, and it’s a loss. But because we are uploading so many, we will get a good chunk of profiles. But now with all of their LinkedIn information.

7. A narrow search

So here Irina showed an example of a very narrow search. A list of software engineers from GitHub, who use Ruby as their main language, their primary language, and they have not even mentioned Ruby on their profiles. Of course, it’s an extreme result, but it shows you that the information is complimentary. 

8. Write a specific personalized message

And now once you have all the ways to contact them, for example, by email, you can write them a specific personalized message. You could mention that they are a member of a group, coded in Ruby, or are part of the National Test Pilots Association.

For example:

I saw that on GitHub you have a large following and primarily code in Ruby”

“We are both members of the “Registered Nurse Jobs” group on Facebook”

“I am reaching out to you as a member of the National Test Pilot Association with an active license” 

The great thing about this approach is how it blends info from different places. By merging GitHub insights with LinkedIn data, you can send out messages that hit home with candidates.

In simple terms, the real-time aggregation solution is a game-changer for recruiters. It’s about using lots of different sources to find the best talent and then reaching out to them in a way that feels personal.

LinkedIn Data Interpretation

In this final segment of the presentation, Irina Shamaeva takes us behind the scenes of LinkedIn’s data operations, revealing some nuances that recruiters should have on their radar. It’s like peeking into the engine room of LinkedIn Recruiter and realizing that it doesn’t always catch every detail on profiles, which can seriously affect how recruiters go about their searches. Essentially, what you see isn’t always what you get.

Take, for instance, the scenario with companies like Apple. As you can see in the image below, sometimes the number of search results can wildly differ based on whether you’re using a dropdown selection or simply typing in the company name as a keyword. The number of results in higher when you type the name of the company than when selecting a company name from a dropdown suggestion list. This is because LinkedIn does not find some profiles of people who work at Apple. 

Now, let’s talk about the art of searching for companies using keywords. It’s not just about typing in the name and hitting enter; there’s a little hack involved. By adding a space, you trigger a text-based search, which tends to bring in more results as it’s a wider search (see the screenshot below). 

But here’s where it gets interesting – you might stumble upon profiles of individuals who work for heavyweights like ExxonMobil or Ford Motor Company, yet LinkedIn doesn’t seem to make the connection when you click on it, almost like a hiccup in the system.

A pretty bizarre result in both cases, ExxonMobil and Ford Motor Company. Irina included text at ExxonMobil and Ford Motor Company and excluded the selection and found people who do work at that company but are not found by the company selection. If you go to this person’s profile and press on the company, it’ll go to the company page, but somehow this association is broken. The same thing with the Ford Motor Company. 

There are also instances where LinkedIn does not find specific properties, skills, or company sizes or types that usually should be on the profile. It’s like navigating through a maze of profiles where important details are hiding in plain sight. What that means is that your search should be very relaxed and should use text much more often than selections. Did you know that a good amount of LinkedIn profiles don’t even bother listing the company size? It turns out that on LinkedIn companies without sizes are about half of the companies. So when you search by company size, you will be missing people who work at half the companies on LinkedIn. 

So because we have search operators it now allows us to write Boolean and we can investigate how many profiles do not have particular values. So here you can search for no company size and years at the company. So the results must have some current company. But company size is not something, is not a field by which you can find it.

Similarly, LinkedIn’s interpretation of seniority and other attributes may not always align with what is evident from the profiles themselves, leading to missed opportunities in searches. 

The images below show examples of searches for senior engineers excluding all the levels of seniority. So these people will not be found by, for example, seniority senior. But by looking at these profiles, it’s pretty clear that they should be senior. 

People without years of experience are also about 60% of all LinkedIn members. Remember that when you’re searching while using all the multiple filters that LinkedIn Recruiter allows.

The key learning here is that recruiters need to dial back on the rigid filters and embrace a more laid-back, text-heavy approach to searching. Keep in mind that LinkedIn misses a lot in interpreting its data. Therefore, avoid selecting values that LinkedIn will suggest to you, especially in filters for titles, companies, and skills. Use Boolean for better coverage and avoid selecting LinkedIn calculated filters, especially for titles, companies, and skills. 


Irina Shamaeva’s presentation on advanced sourcing techniques on LinkedIn offers valuable insights and strategies for recruiters who want to improve their sourcing process and find the best talent for their roles. By following her tips and advice, you can leverage LinkedIn’s features and data to optimize your search results, discover more diverse and qualified candidates, and overcome the limitations of LinkedIn’s data interpretation.

By exploring targeted and open-ended search approaches, you will gain a deeper understanding of how to navigate the nuances of LinkedIn’s search functionality. Shamaeva’s emphasis on tailoring search strategies to fit specific recruiting needs is a real eye-opener. It’s all about being flexible and thinking outside the box when it comes to finding top talent.

Moreover, her explanation of hidden search operators and real-time aggregation solutions provides practical tools for sourcing professionals to expand their candidate pools and access hard-to-reach talent. Understanding how to leverage these tools effectively can give recruiters a competitive edge in identifying and engaging passive candidates.

Furthermore, insights into the limitations of LinkedIn’s data interpretation shed light on the importance of critical thinking and meticulous search refinement. It’s a reminder to always keep a critical eye on your search results and refine them to perfection. While sourcing in LinkedIn Recruiter, avoid selecting values, especially for titles, companies, company sizes, seniority, skills, and other calculated filters – use Boolean instead. By doing so, you can ensure you’re getting the most accurate and relevant results, leading to better hires.

All in all, this session is a must-see for anyone in the sourcing and recruiting game. All readers of our blog can benefit from incorporating these insights into their recruitment strategies, ensuring they remain at the forefront of industry best practices and effectively meet the demands of their organizations.

If you want to learn more from Irina Shamaeva, you can subscribe to her course, LinkedIn Recruiter Mastery. You can also follow her on LinkedIn for more updates and insights.

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