Candidate Sourcing Techniques

I’ve played around with almost all known AI sourcing tools and most of them make the same mistake. They jump to a list of candidates. As a recruiter finding a list of candidates is actually not necessarily the hardest part of my sourcing. I can build a search string relatively quickly (more quickly than a natural language prompt). What I really need from an agentic sourcing tool is market mapping. I want to know who my talent competitors are, who are their competitors, what companies have the highest hiring success rate in my org, what companies fall into the right stage and have the highest talent density for us? Then I want to deeply map roles and hierarchies in those businesses to find the right talent. THEN I want my long list of candidates to approach. But only when I’ve done all those steps (and more) can I confidently say I’m engaging with the right talent. Agentic sourcing tools skip past too many first principles of sourcing and jump straight to the answer. I want to see the workings out.

I just spent three hours sourcing for a senior data analyst role. I looked at maybe 400 profiles. Want to know how many I was able to reach out to? Nineteen. And here’s the thing that frustrates me as a recruiter: I have no doubt that dozens more of those 381 candidates were genuinely qualified. Probably some were perfect. But I couldn’t tell, because the keywords I needed weren’t on their profiles. And when you’re working a single role against a clock, you can only dig so deep before you have to move on. I want to share this because I think a lot of really talented people are getting passed over for reasons that have nothing to do with their ability, and everything to do with how their profile is written. Here’s how sourcing actually works for most technical roles in 2026. When I’m hiring for, say, a senior data analyst with Adobe Analytics experience, I’m running a Boolean search in LinkedIn Recruiter. Something like (“Data Analyst” OR “Analytics Engineer”) AND (“Adobe Analytics” OR “Adobe Customer Journey Analytics”) AND (JavaScript OR jQuery) AND (SQL OR Python). Only the profiles containing those specific terms surface in my results. If your profile says “responsible for digital analytics across the organization,” I’m not going to find you, even if you’ve been doing exactly this work for ten years. If it says “5 years building Adobe Analytics implementations with JavaScript tagging, AEP integrations, and SQL-based reporting in Databricks,” you’re on page one and I’m reaching out. This isn’t a judgment on you as a candidate. It’s a mechanical reality of how these platforms work, and once you understand it, your inbound recruiter activity should change pretty dramatically. A few practical things that make a real difference. 1. Your summary and experience descriptions are some of the most valuable real estate you have. Use them to name the specific tools, platforms, languages, frameworks, certifications, cloud providers, and domains you’ve actually worked in. Real product names matter. “Cloud experience” is invisible to a Boolean search. “AWS (EC2, S3, Lambda, EKS), Terraform, GitLab CI/CD” gets you found by every recruiter sourcing for that stack. 2. The same is true on your resume. Recruiters running searches in ATS platforms and job boards are using the same kinds of queries. If a keyword isn’t on the page, you’re not in the result set, no matter how qualified you are. 3. Spell things out, including acronyms and their long forms. SRE and Site Reliability Engineering. CKA and Certified Kubernetes Administrator. Different recruiters search different ways, and you want to be discoverable either way. 4. Put your tech stack inside each role description, not just in a skills section at the top. Recruiters often want to know what you’ve used most recently, and burying that in a single list at the top can cost you.  

Most Recruiters hit a wall mid-search because they don’t track what they’ve already done. Whenever I start a new search, I open a fresh Google Doc, which becomes my dedicated Sourcing Working Doc. This becomes the central hub for the search. I’ll drop in: • Key notes from the job briefing • Feedback and links to benchmark profiles • Competitor mapping and research • Boolean strings, organised by keyword category    This allows me to properly calibrate my understanding of the role early on, using insights from the intake call and benchmark profiles (e.g. members of the current team or profiles the client has given feedback on). Plus, it allows me to organise my Boolean strings into categories, making everything easy to manage. Let me give you an example. Last year, I ran a search for a Chief Product/Digital Officer at a Test & Instrumentation business undergoing a major digital transformation. Here’s how I structured part of my Boolean working doc: 𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝘆 𝟭 - 𝗝𝗼𝗯 𝗧𝗶𝘁𝗹𝗲: ((“Product” OR “Digital”) AND (Director OR Global OR Head OR Chief OR Exec OR Executive) AND (Transformation OR Software)) OR (CPO OR CDO OR "Chief Product Officer" OR "Chief Digital Officer") 𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝘆 𝟮 - 𝗧𝗲𝘀𝘁 & 𝗜𝗻𝘀𝘁𝗿𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: (“test and measurement” OR “T&M” OR “test & measurement” OR Sensors OR Instruments OR “Test and Instrumentation” OR “Test & Instruments” OR “Test and instruments” OR “Test & instrumentation” OR “T&I” OR “Test software” OR “Test Systems” OR “Test, Verification & Validation Engineering” OR "Measurement Systems") 𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝘆 𝟯 - 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻:  ("digital transformation" OR "digitalisation" OR "digitalization" OR "Digitised" OR "Digitized" OR "Software transformation" OR "Digital Innovation" OR "Digital Enablement" OR "Digital Strategy") As the search progressed, I naturally came across alternate keywords and synonyms, which I added under the relevant categories. Another useful feature I add to my Working Docs is a Sourcing Process Checklist. This consists of two lists: 1. Sourcing Channels - WHERE I’ve searched, or the channel I’ve sourced (ATS, LinkedIn, X-Ray, SeekOut, PeopleGPT, etc.) 2. Sourcing Techniques - HOW I’ve searched, or what technique I’ve used (Narrow First, Then Wide; Implicit Search; Natural Language Search; etc) I tick these off as I go, which stops me from retracing the same steps or repeating ineffective searches. Ultimately, the Sourcing Work Document becomes a living repository of my sourcing journey. Yes, it requires an upfront investment of time. But the payoff is worth it: better clarity, smarter sourcing, and faster turnaround on high-quality shortlists. And by keeping all my Working Docs in a central folder, I can quickly re-use them for similar roles or share them with colleagues if needed. #sourcingworkingdocs #sourcing

𝗛𝗼𝘄 𝗕𝗼𝗼𝗹𝗲𝗮𝗻 𝗦𝗲𝗮𝗿𝗰𝗵 𝗖𝗮𝗻 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 𝗬𝗼𝘂𝗿 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗟𝗲𝗮𝗱 𝗦𝗲𝗮𝗿𝗰𝗵 Struggling to find the right leads on LinkedIn? Boolean search might be your secret weapon. It’s a simple yet powerful way to refine your searches and uncover highly relevant prospects... without scrolling through endless results. Here’s how it works: Boolean search uses a few logical operators to combine or exclude keywords. When applied to LinkedIn searches, it helps you pinpoint exactly who you’re looking for. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗕𝗼𝗼𝗹𝗲𝗮𝗻 𝗢𝗽𝗲𝗿𝗮𝘁𝗼𝗿𝘀 1. 𝗔𝗡𝗗 – Find profiles that include all your specified terms. Example: marketing AND manager Results: Profiles containing both “marketing” and “manager.” 2. 𝗢𝗥 – Find profiles that include any of the specified terms. Example: sales OR business development Results: Profiles with either “sales” or “business development.” 3. 𝗡𝗢𝗧 – Exclude specific terms. Example: software engineer NOT intern Results: Profiles with “software engineer” but without “intern.” 4. 𝗤𝘂𝗼𝘁𝗮𝘁𝗶𝗼𝗻 𝗠𝗮𝗿𝗸𝘀 ("") – Search for exact phrases. Example: "product manager" Results: Profiles with the exact term “product manager.” 5. 𝗣𝗮𝗿𝗲𝗻𝘁𝗵𝗲𝘀𝗲𝘀 () – Group terms to control the search logic. Example: (marketing OR sales) AND director Results: Profiles with “director” and either “marketing” or “sales.” 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀 ➡ Looking for decision-makers: "marketing director" OR "head of marketing" ➡ Excluding irrelevant roles: "software engineer" NOT intern NOT junior ➡ Finding leads in specific locations: "digital marketing" AND "London" 𝗪𝗵𝘆 𝗜𝘁 𝗪𝗼𝗿𝗸𝘀 Instead of sifting through hundreds of irrelevant profiles, Boolean search lets you focus your efforts on more promising leads. Combine it with LinkedIn’s filters, such as location or industry, for even better results. 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 Boolean search is a game-changer for anyone serious about lead generation on LinkedIn. Why not try it out today? A few tweaks to your search will save you time and help to uncover opportunities you maybe never knew were there. Have you used Boolean search before? Share your experience in the comments! Ps. Download the document for future reference 👍

Job boards do not yield the best candidates. Here are 5 of the BEST strategies we use to source candidates at Recruiter Unlimited: 1. Warm networking mining. Our team has connections. Our connections have connections. We start here first. 2. Targeted Outreach (without LinkedIn Recruiter). We pick 3-5 competitive /adjacent companies and reach out to passive candidates with customized messaging that states the value prop up front. 3. "NO" candidates get special treatment. If a candidate gets to the interview stage and isn't selected, we give them the royal treatment. We provide resume templates, interview prep guides, and resources to help them. We believe this is not just the right thing to do, it keeps the good ones in our orbit. 4. Outplacement. A win-win. Our outplacement arm supplies our recruiting arm with candidates who are ready and excited to find their next thing after getting let go. 5. Social media content. From our podcast to LinkedIn posting, content is King and allows us to market faster and more effectively than other recruiting agencies. --- FOR CANDIDATES: Please for the love, update your LinkedIn. It's your best asset. Not your resume. Your LinkedIn profile. FOR HIRING COMPANIES: If you don't have a strategic sourcing plan OR don't like your recruiting agency, we'd love to chat! P.S. What's the hardest thing about finding top talent in 2026?

“We paused sourcing because interviews started.” …said no high-performing TA team, ever. Most teams front-load sourcing. They go hard for a week → interviews kick off → sourcing stops. Then candidates ghost, hiring managers change their minds, and—surprise—a new req shows up. Cue the chaos. Quick! Post the job. We need to move fast! (We’ll save the “speed of job postings” debate for another day…) Sound familiar? But how do you build a sourcing culture that actually lasts? Here are 3 simple (but powerful) ways: 1. Track momentum, not just hires 👉 # of relevant leads added 👉 # of passive conversations started 👉 % of sourced candidates reaching final stage Keep the pipeline visible, even when interviews are humming. 2. Time-box weekly sourcing sprints 👉 30–90 min blocks per week, even during offer stage 👉 Monthly challenge: everyone sources live together 👉 Live-share tough searches and learn from each other Non-negotiable time. No one books over it. Period. 3. Reward consistency, not just outcomes 👉 Shoutouts for Boolean brilliance 👉 Peer-nominated “pipeline builders” 👉 Celebrate sourcing wins, even if the hire doesn’t happen (yet) So, if you want to break the stop-start sourcing cycle: → Reframe the purpose → Make progress visible → Normalize the habit → Celebrate the behavior → Build before it’s urgent Because when sourcing stops too soon, you don’t just lose time, you lose momentum. What’s one thing your team does to keep sourcing consistent, even when interviews are full swing? 👇 Drop your team rituals or hacks in the comments. Let’s crowdsource some ideas.

I cost a client a £120k hire last week. Not intentionally. My AI sourcing tool did. I didn't even know. We were searching for a senior ML engineer at First2 Group. PeopleGPT generated the Boolean string in 8 seconds. I ran it without checking. 0 results. The role should have returned 400+ profiles. I rebuilt the string manually. Found 312 candidates. One of them was perfect. First-author NeurIPS paper. 6 years applied ML. Actively looking. The AI had excluded them with a single misplaced parenthesis. They got the interview. My client made the hire. If I hadn't caught it, my competitor would have placed them first. (Most recruiters don't verify AI-generated Boolean strings before running them. The syntax looks valid. The logic is broken. I've reviewed 47 profiles in a single session and found this pattern repeatedly — across 28 businesses at First2 Group, AI strings fail silently.) I've tested AI-generated Boolean strings across 28 businesses. More than half return logical errors that aren't visible until you run them. PeopleGPT claims 70% faster sourcing. LinkedIn data shows AI tools triple recruiter productivity. 𝗙𝗮𝘀𝘁𝗲𝗿 𝘁𝗼 𝘁𝗵𝗲 𝗪𝗿𝗼𝗻𝗴 𝗮𝗻𝘀𝗪𝗲𝗿 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝗳𝗲𝗮𝘁𝘂𝗿𝗲. Here is what I changed: every AI-generated Boolean string now gets a manual audit before it runs. Bracket count. Operator precedence. NOT exclusions. 90 seconds. Every time. The EU AI Act classifies automated candidate screening as high-risk from August 2026. AI-generated search strings feeding those screens sit just below that threshold. Nobody is auditing them. Boolean is not a legacy skill. It is what saves you when the AI fails in front of a client. Do you verify AI-generated Boolean strings before running them — or do you trust the output? Save this before your next sourcing brief.

Most job seekers are searching the wrong way—typing job titles into search bars and hoping for the best. 🚨 There’s a better way. It’s called *Boolean search* It’s the exact method recruiters use to find top candidates - faster But, YOU can use it to reverse engineer their search or to find *better* jobs—faster. 👇10 Boolean Search Basics (that could save you hours of scrolling) 🎯 Find jobs in a specific industry & city → "sales director" AND ("tech" OR "software") AND "San Francisco" Only shows sales director roles in tech/software companies in SF. 🎯Exclude job types you don’t want → "project manager" AND "Los Angeles" NOT "contract" Filters out contract roles, so you only see full-time positions. 🎯Find remote jobs (and ignore entry-level roles) → ("data analyst" AND "remote") NOT ("internship" OR "junior") Focuses on remote data analyst jobs while avoiding entry-level roles. 🎯Search multiple job titles at once → ("product manager" OR "program manager" OR "project manager") AND "Seattle" Expands your search to three similar job titles in Seattle. 🎯Find recruiters in your field → (recruiter OR "talent acquisition") AND ("finance" OR "investment banking") Connects you with recruiters who specialize in finance & banking. 🎯Locate hiring managers at a specific company → ("hiring manager" OR "team lead") AND "Tesla" AND "engineering" Helps you find decision-makers at Tesla in the engineering department. 🎯Search for companies currently hiring → "We're hiring" AND ("cybersecurity" OR "network security") AND ("remote" OR "hybrid") Finds job posts from companies actively looking for cybersecurity professionals. 🎯 Discover networking events in your industry → ("digital marketing" OR "SEO") AND ("conference" OR "webinar" OR "networking event") NOT "paid" Finds relevant events for digital marketers without paid ads cluttering results. 🎯Find companies growing fast (new jobs soon!) → "hiring surge" OR "expanding team" OR "new office" AND "biotech" Targets biotech companies that are scaling up—new roles are coming. 🎯Boolean search combo → ( ("UX designer" AND "Austin") OR ("Google" AND ("hiring manager" OR "design lead")) OR (recruiter AND "user experience") ) NOT ("junior" OR "assistant") A power search for UX jobs in Austin, hiring managers at Google, and UX recruiters—while skipping junior-level roles. Better searching = better results ______ ♻ Share this with someone who could use the tip 💬 Drop a comment if you’d like to hear more about this and I'll cover it on my YouTube Channel

Most recruiters are not sourcing. They are searching the same database with different keywords. That works when the role is common and the talent pool is large. It fails when the role is hard to fill. For difficult roles, the problem is usually not a lack of candidates. The problem is that the best candidates do not describe themselves using the exact title, skills, or keywords written in the job description. A backend engineer may have solved large-scale payment failures without using the words “distributed systems” anywhere on their profile. A security engineer may have built fraud detection systems but never held a security title. A strong engineering manager may still call themselves a staff engineer because their company does not use management titles. Keyword search misses these people because it looks for how someone describes themselves. Good sourcing looks for what they have actually solved. Before starting a search, answer these three questions: What problems will this person need to solve? What evidence would show they have solved those problems before? Where would someone with that experience likely have worked, built, written, spoken, or contributed? Then search using that evidence. Look for products built, systems scaled, migrations completed, incidents handled, teams led, and previous company environments. Job titles are weak signals. Keywords are weak signals. Evidence of relevant work is a stronger signal. For easy roles, searching may be enough. For hard roles, you need to investigate.

A writer recently asked if we had any go-to tools for sourcing credible studies and reports. While there’s no magic bullet (yet), we do have some trusted methods that I’m always happy to share. And yeah, I am *happy* to spill our secrets here. 😏 Because if you’ve ever been personally victimized by a stat round-up citing a “study” of 14 people on their email list from 2004 🙋 -- you get it. Let’s raise the standards, people. Here are 7 underrated ways to find credible research. 🧠 1. Google Scholar (but smarter) Use "site:.gov" or "site:.edu" with keywords like “report” or “survey.” You’ll find reliable government and academic data. 🏛️ 2. Skip the aggregators like Statista and go straight to the source. Check out: --Analyst firms (Gartner, McKinsey, etc.) --Public databases (OECD, World Bank) --Industry orgs (IAB, Content Marketing Institute) Their site search bars and newsletter archives are your friends. Exec summaries often have what you need. 🗂️ 3. Search by filetype Try: --"your topic" filetype:pdf for whitepapers, reports --"your topic" filetype:ppt for internal decks and analyst presentations that somehow ended up online 👀[this happens more than you’d think] 🎤 4. Conference decks = new stat ‘goldmines’ Recent event decks (e.g. from MozCon, SaaStr, BrightonSEO, etc.) often contain recent data. Google "[event name] + site:slideshare.net" or “[event] deck” to hunt them down. 👀 5. LinkedIn > Google (sometimes) Subject-matter experts love sharing fresh benchmarks and survey results. Try searching: --"[your topic]" and "%" --"[your topic]" and "survey" Bonus move: Ask in the comments for the full report. People sometimes reply. 🧵 6. Reddit & niche communities r/marketing, r/dataisbeautiful, Indie Hackers… these are full of people casually dropping data treasure. Use: site:reddit.com “your topic” data or report or study 📩 7. Ask directly If someone mentions “internal research” or “according to our data”... just ask. I've DM’d people before and walked away with better sources than any roundup black hole could give me. — With a little digging, you can find research that adds real credibility (and freshness) to your content. Got a favourite research rabbit hole I missed? Share it below!