A Few Words About Yandex

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Yandex.com is the third major global English search engine (it searches – obviously – in Russian – and other languages as well).

As I am preparing the 6th (!) edition of the “300 Best Boolean Strings” ebook, I am reviewing the chapter on Yandex. Let me share it with you.

Here is the Syntax Description for Yandex.

Like other search engines, Yandex supports the site: operator and relies on quotation marks (“”) to indicate a phrase. This search works on Yandex:

site:linkedin.com/in “Greater Philadelphia Area” “Inside Sales Representative”.

Alas, Yandex has indexed very few LinkedIn profiles.

Yandex has a whole array of X-Ray operators.

To search for files of a specific type on Yandex, use the mime: operator:

mime:xlsx “new york” “vice president of” “supply chain” phone email

Yandex has some unique search operators as well. Here are some Yandex search abilities that stand out.

The exclamation mark preceding a word tells Yandex not to modify the word:

buy !apples wholesale

The plus in front of a “stop” (i.e., insignificant) word makes sure the word is included.

what to do +if the computer shuts down

The square brackets tell Yandex to find the words inside them in a particular order:

tickets [from london to paris] – in the results, London will always precede Paris.

The operator lang: narrows results to pages in the language. Use two-letter language abbreviations: passport lang:en.

You can select a region, sort by date, or narrow to a recent date range if, after searching, you press the “advanced search” icon. Yandex has search operators for the date range as well.

Search settings allow entering several “preferred” sites, which will rank high (but not much else).

Unfortunately, Yandex has lost its specific proximity operators, likely, for the lack of usage. But the Asterisk works similarly to Google’s.

I recommend using Yandex for image search and reverse image search – currently, results are better than in Google or Bing. When you run reverse image search using someone’s photo, Yandex will involve facial recognition! It works even better if you run it from a Russian IP address.


The Best of 2021

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Thank you for reading my blog! 1,80000 views and counting.

1,600 Recruiters took The CDSP Sourcing Certification Program for Recruiters and Teams. See feedback.

200 researchers bought the one and only book on CSEs – Custom Search – Discover more: 1st Edition. It took a year and 1/2 to write.

LinkedIn Operators work in LinkedIn Recruiter Job Title or Company Links (thanks Aaron Lintz!)

The last and most read 20 LinkedIn Profile X-ray Strings for 2022. Our cat R2D2 messed up with it and now wants to have his own blog!

Github – Tech Recruiters Paradise sponsored by AmazingHiring.

X-Ray webinar recording.

Most read post ever – Hidden Profiles

8K+ people joined the FB Boolean Strings Group

I am open to sourcing projects(10K+ views).

Follow Cyb_detective. #OSINT

Email Collector

Watched “New Tricks” and “Line of Duty”. British TV is awesome!

Happy sourcing!



20 LinkedIn Profile X-ray Strings for 2022

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By current job title:

site:linkedin.com/in intitle:”salesforce consultant”

By current company:

site:linkedin.com/in intitle:oracle

By any company:

site:linkedin.com/in “amazon graphic”

Company past not present

site:linkedin.com/in “ibm graphic” -intitle::ibm 

Group member:

site:linkedin.com/in “sourcing summit graphic”


site:linkedin.com/in “cissp graphic”


site:linkedin.com/in “american hospital association graphic”


site:linkedin.com/in “Lomonosov Moscow State University (MSU) graphic”


site:linkedin.com/in “recommendations given”

Presence of “Honors and Awards”

site:linkedin.com/in intext:”honors and awards”

Good grades:

site:linkedin.com/in “cum laude” OR hons OR honous OR honors” OR “first class” OR “1st class” OR bien OR “2:1”

First and last name:

mary AROUND(2) jones

Current location:

“new orleans” AROUND(5) connections site:linkedin.com/in

Job location

site:linkedin.com “work location * san francisco bay area”

Public Gmail address

site:linkedin.com/in “gmail.com”

Job title at a past company:

site:linkedin.com/in “chief * officer” AROUND(4) microsoft -intitle:chief -intitle:microsoft.

Service providers

site:linkedin.com/in “work preference”

“I accept direct messages and business inquiries by anyone on LinkedIn for free even if we’re not connected.”

People recommended by Donna site:linkedIn.com/in “Click here to view Donna Svei Executive Resume Writer’s profile”


site:linkedin.com/in 🌈

Scrape X-Ray for Research Including Diversity

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I have a demanding client who keeps sending me challenging sourcing requests, needing more than Boolean Search in LinkedIn Recruiter can achieve. (I love those.) Her last three requests have been – all in Europe – to find:

  1. Frontend Developers who have LinkedIn recommendations.
  2. Backend Developers who used to work as Research Assistants and have good grades (like “cum laude”).
  3. Software Engineers with under ten years of experience who are Diversity (not just women, she insisted).

The expectation was to get lists with hundreds of profiles. I was able to deliver, but it involved a lot of head-scratching.

You cannot search for Recommendations with any LinkedIn account. You cannot look for school grades. If you search for an OR or “good grades” terms, you will only find a small portion of those who have put it elsewhere on the profiles (good for them!). And, LinkedIn does nothing to help search for Diversity.

Since many diverse categories represent the minority of professionals in the desired professions, just searching on LinkedIn and screening the results for Diversity is too time-consuming. I had to go with X-Ray.

So, for Task 1, I X-Ray LinkedIn, patiently, by country, for the words “recommendations received” to be there. I scrape results with Instant Data Scraper and filter out false positives. Now I have a list of promising LinkedIn URLs. A way to go is to paste the list to SalesQL‘s (brilliant!) upload function. Its export features more fields from profiles than I have seen anywhere else. (An alternative is Phantombuster.) As a result, I have a rich Excel file, which I clean up, sort, filter, and select my prospects, whom I can email. Because of this additional step, my search strings can be imprecise, and I get results which won’t surface in a tighter search.

Task 2, same story with various “cum laude” words.

Task 3 is more complicated because I need to find “Diversity Indicators” for each type of Diversity. But the principle is the same. I start with researching Diversity Identifiers such as women’s names, relevant organizations, diversity schools, etc. Note that simple scraping helps here as well. I can ask Google questions and get lists of terms as Featured Snippets. Then I use an OR of the terms both on Google and LinkedIn.

Another type of sourcing to be done with scraping is searching for job stability, sadly, never offered by any sites.

It is time to update your scraping skills! Join me for the upcoming class Web Scraping For Recruiters on January 5th, 2022.

Talent Sourcing and #OSINT

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Only recently (I think) have we realized that our paths cross. Both Sourcers (and Recruiters who source) and OSINT professionals collect and examine online data, cross-reference sources, use Google and reverse image search, and more.

The main difference appears to be the following:

  • Recruiters search for all people matching a requirement
  • OSINT specialists search for a person of interest and everything about them and their environment.

Visually, I imagine it as tables vs. trees:

  • Recruiters collect table data, one record per candidate
  • OSINT people collect tree-like data representing a person’s personal and professional life and the same of their close relatives and coworkers, etc.

Each side is ahead of the other in terms of skills and mastered tools. Generalizing, I would say that Sourcers know Google, LinkedIn, and contact-finding better. OSINT specialists are better at analyzing geo-spatial information, image analysis, use more technical tools, and write code more often.

We can teach each other complementary skills. We can speak at each other’s conferences. Recruiters may volunteer to look for missing people.

P.S. Cyber Detective and I have just rebranded a LinkedIn group to cover #OSINT topics. You are welcome to join!

Notes from the LinkedIn Field

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[Edited Dec 8th: it is back working! And the search right now is truly Boolean. Enjoy while it lasts!] The field of LinkedIn.com search is currently, unfortunately, full of bugs. Following up on and expanding my last post, here are some observations.

  1. Often, results do not include some or all of the 3rd level connections. It seems random to me; any insights from the analytical minds are welcome!
  2. Sometimes, NOT is not respected. This search for cats not dogs produces a profile with the word dogs. Also seems random.
  3. Terms in the Keyword field are heavily interpreted – have been for a long time. Try, for example, java engineer (NOT java NOT engineer) – it brings up over 200 results. You never know what LinkedIn may decide sounds like a title. Here is how to avoid the interpretation – LinkedIn’s “Verbatim” mode (not entirely though; things may still happen):
  • Put every term in the quotes.
  • Use as many parentheses as you can.
  • Use the explicit AND.

Then, the results will get better.

Note that not all of us are affected by the above (see some differences in the comments.) Some members get reasonable results (but the interpretation part affects all, so use your quotes.)

LinkedIn Boolean Is Crazy

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Searching on LinkedIn costs many Recruiters low self-esteem. When you see results you did not expect, you may blame yourself for searching “the wrong way.” But it is rarely the case.

What has been happening on LinkedIn and LinkedIn Recruiter (LIR) lately is beyond my (reasonably high) IQ.

Let us compare some searches on LinkedIn.com and in LIR. We vaguely anticipate that Recruiter should produce more results because it is expensive. But which ones? We still see “LinkedIn Member” instead of the names for profiles that are out-of-network on LinkedIn; so what exactly is hidden? (In the past the results’ numbers were the same, and only the visibility was different.)

Examples confirming that LIR is “better”:

  • AWS – 1.3M+ results on LinkedIn.com and 3M+ in LIR.
  • vp marketing (NOT sales) railroads – 10 on LinkedIn.com, 407 in LIR.
  • cats dogs – produces  3.6K on LinkedIn and a whooping 100K+ in LIR. No pay, no cats, no dogs! Who are the mysterious cats and dogs people only found for money? I have no idea.

But sometimes, it is the other way around:

  • janitor – 211K on LinkedIn, only 110K+ in LIR.
  • cats NOT dogs – 210K on LinkedIn.com, only 150K+ in LIR.

The discrepancy is all over the place. I doubt it is LinkedIn’s conscious effort to get more dollars out of its customers.

Have LinkedIn Developers activated a random number generator while celebrating something? Or are their servers under attack?

In the meantime, try our tool Social List (cc upfront, 7-day trial, $50/month after) to generate excel exports of LinkedIn and other Social Network X-Ray results; it also has a Contact Finder. The tool will not be affected by any LinkedIn bugs, I promise.




Big Data for Executive Search

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Research is a vital part of executive search. You need to be knowledgeable about the industry, the candidate’s background, and the role at your company. You need to find their contact info.

Here are some resources to help you with gathering intelligence.

Any data sets you could share with us?

Several Contact Finders, including contactout.com offer advanced people search dialogs and Excel export (plus contact-finding, of course). Here is what it looks like:

I am sure these profiles can be found on LinkedIn, but LinkedIn does not offer contact info or downloads.

Similarly, SalesQL works over LinkedIn or X-Ray search, scrapes the details and enriches.

In my experience it has the best coverage among Contact Finders.

Join us on December 1st, 2021, for a complete coverage of sourcing for executives!

Change Your Thinking, Rewrite Your Search Strings

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Successful Sourcers know the main – secret! – principle of productive search: “Imagine the words and phrases exactly as they should appear in the target results. Then, use those words and phrases in your search”.

We also call it “Visualize Success”.

Rarely do Recruiters get training in the “Visualize Success” skill, yet it is the #1 factor in finding target data even if the data is well-hidden. Once you start thinking this way, your search results world will turn around accordingly, finding never-seen relevant pages. (It implies thinking while searching 😉 )

Here are two practical use cases of implementing the “Visualize Success” principle (that you can start today):

  1. While constructing X-Ray search strings, if possible, review several sample pages to see what information they contain. Prepare your imagination to work, close your eyes, and picture an ideal (or acceptable) candidate’s profile – which words and phrases are there? Use those in X-Ray. Once you uncover and test an X-Ray template and educate yourself on target keywords, you can get to work.
  2. An additional, little-used, source of professional data is online contact lists or directories of organization members, conference attendees, and company staff. Sometimes, pages with lists and directories are posted on the surface web in an Excel format, but often, also in PDF or HTML. You can Google for them.

If you have obtained a list of professionals with contact emails, you can discover their profiles on LinkedIn by uploading a CSV list of the emails (and any names) into your Contacts. After an upload you will know the following about each person:

  • Their LinkedIn URL
  • Confirmed email address
  • Knowledge that they come from that list (e.g.,, spoke at an industry event, etc.)

Nice, isn’t it? So Googling for lists of professional contacts is an excellent idea as a step in your sourcing process.

To find lists of professionals, prepare your imagination again. Clearly, Googling for “contact lists (<keywords>” will only land you on contact vendor databases ads. Everyone’s usual “Googling” using a few terms will never take you there.

The best list-finding search strings have two or three values of the same category (or more than one category) such as:

  • names
  • companies
  • job titles
  • email extensions
  • phone area codes
  • (etc.)

You can Google for lists of values like country phone codes or top companies in an industry and then use the values in search strings.

Here are some examples that work like magic (even though I have not used any advanced operators):

What are some clever Google search strings that helped you find unique  contact lists? Please share.

How to Clean Up CSV Export

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As Sourcers, we have to work with exported data. LinkedIn Recruiter, Instant Data Scraper, and other scraping tools provide export to CSV with the wrong coding, that may look like this:

Looks familiar? Here is how to straighten it out (thanks to David Galley for the tip!)

Step 1. Open a new Excel file and choose Data/From Text/CSV:

Upload the CSV file.

Step 2. Choose “Unicode (UTF-8)” and load:

Done. The coding is straightened out:

My favorite scraper is the contact finder SalesQL because it provides:

  • Excellent coverage across industries and locations
  • Private and work emails and phone numbers
  • Adding lists of LinkedIn URLs from a LinkedIn search as well as LinkedIn X-Ray
  • Generous 100 free credits per month
  • Bulk LinkedIn URL upload

The dashboard looks like this:

Try it out if you haven’t!