Algorithmic Job Fit Predictors

When it comes to using AI to match candidates with jobs, more accurate/predictive AI is better, right? Not necessarily. One data-driven study would suggest the answer is no. I recently read Co-Intelligence, Living and Working with AI by Ethan Mollick, which I highly recommend. In the book, Ethan features a study by Fabrizio Dell’Acqua titled, "Falling Asleep at the Wheel: Human/AI Collaboration in a Field Experiment on HR Recruiters" in which 181 experienced recruiters were hired to collectively review nearly 8000 resumes for a software engineering position. Of note: the recruiters were incentivized to be as accurate as possible. The recruiters received algorithmic recommendations about the job candidates but the quality of these AI recommendations was randomized between 1) perfectly predictive AI; 2) high-performing AI; 3) lower-performing AI; and 4) no AI. Of critical importance to the study, recruiters were aware of the type of AI assistance they would be receiving. Key findings include: 1. Recruiters with higher-quality AI performed worse in their assessments of candidates in relation to the job than those using lower-quality AI. They spent less time and effort in their evaluations of each candidate, and they tended to blindly trust the AI recommendations. 2. Recruiters with lower-quality AI "exerted more effort and spent more time evaluating the resumes, and were less likely to automatically select the AI-recommended candidate. The recruiters collaborating with low-quality AI learned to interact better with their assigned AI and improved their performance." These findings suggest that when users have access to high-quality AI (or at least believe they do), they are indeed in danger of "falling asleep at the wheel," where they become overly reliant on AI, and reduce their attention, effort, and critical thinking - which can negatively impact outcomes for all involved. As we increasingly integrate AI into work, it's important to maintain a balance between technological support and human skill/expertise. Instead of aiming for (or claiming to have!) "perfect" AI, perhaps our goal should be to develop systems that enhance human decision-making and keep users actively engaged and thinking critically. What do you think? Check out the full details of the study here: https://lnkd.in/eGaTmTEi #AI #matching #criticalthinking #futureofwork

Our in-house hiring platform was supposed to solve healthcare hiring. Accidentally, we created the future of talent matching. The Healthcare Problem: Saudi hospitals need 40,000+ healthcare professionals by 2030. Traditional recruiting can't scale. Our Initial Solution: AI-powered skill matching for clinical roles. What We Discovered: The algorithm that matches doctors to specialties also matches: - Engineers to project requirements - Consultants to client needs - Researchers to grant opportunities - Executives to board positions The Technical Breakthrough: Context-aware competency modeling - Multi-dimensional skill vectors - Cultural fit algorithmic assessment - Career trajectory prediction - Performance outcome correlation The Business Impact: - 87% reduction in hiring time - 94% candidate-role compatibility - 156% increase in retention rates - $2.3M average cost savings per implementation Sometimes the best innovations come from solving one problem really well, then realizing you've solved a dozen others. What unexpected applications have you discovered in your technical work? #Hiring #TalentTech #AI #Innovation #HealthcareHiring

"I Know AI in CV Screening May Be Useful, But I Don’t Trust the Output" As a Talent Acquisition Head, you've likely encountered the growing buzz around AI-driven tools for CV screening. Many talent leaders worry about AI missing key human insights or filtering out qualified candidates based on rigid algorithms. You might wonder, "Can AI truly understand the nuances of experience, skills, or cultural fit?" The truth is, AI can be highly effective when used as a complement to human judgment—not a replacement. Here's why: AI Enhances Efficiency: AI automates time-consuming tasks like scanning hundreds of CVs for basic qualifications, allowing recruiters to focus on high-value activities such as engaging top candidates and conducting interviews. Data-Driven Insights: AI doesn’t just screen resumes—it provides insights by analyzing patterns and predicting a candidate’s suitability based on historical hiring data. However, it’s essential that AI models are continually trained and improved to align with your evolving hiring needs. Human Oversight is Key: Trusting AI doesn’t mean removing human oversight. The best AI tools work alongside recruiters, giving them more time to make thoughtful, strategic decisions. AI can highlight potential candidates, but final decisions should always involve human intuition and consideration. At ai.skanjo.com, we've developed a unique approach that ensures AI enhances rather than replaces human decision-making. Our core algorithm handles the decision-making by comparing each CV directly with the job description (JD), rather than comparing CVs against each other. We’ve integrated a large language model (LLM) to gather context and build data-driven insights, going beyond simple matching scores. Recruiters get not only a match percentage but also detailed insights on why a candidate fits or doesn’t fit the role. This AI-driven reasoning gives talent acquisition teams deeper understanding and clarity, allowing them to make more informed, data-backed hiring decisions. By choosing the right AI-powered solution, you can improve efficiency without sacrificing the human touch. The key lies in trusting the technology as an assistant—not as the final decision-maker. When integrated thoughtfully, AI can help you create a fairer, faster, and more effective recruitment process, giving you the best of both worlds.

🧠 When Data Spoke Louder Than a Gut Feeling. A few weeks ago, I was speaking with a Talent Acquisition lead from a mid-sized tech firm. She told me something that stuck.... "Harsh — our hires look great on paper, but performance tells a different story" Their team had been struggling to fill Data Analyst roles. Every resume looked right — fancy tools, neat project lists — but 3 months into joining, many hires couldn’t handle the business-critical analysis their role demanded. So, we decided to experiment. We ran AI Skills Match on their past 5 hires — comparing each candidate’s resume to the actual job context using iMocha’s AI Skills Match The insights hit hard: Only 1 out of 5 of past hires was a Strong Match to the job’s real skill DNA. And he who was strong match performed 2.3x better in business impact metrics. The AI didn’t just look for “Python” or “Power BI” — it understood how candidates applied those tools in solving business problems, interpreting data patterns, and driving insights that mattered. That success got everyone’s attention. So, the HR head decided to run a deeper analysis — this time, across 20 past roles filled over the last year: Analysts, Project managers & Software Engineers. The data told a clear story: 1. Only 38% of past hires were a Strong Match to their role’s actual skill DNA. 2. The algorithm wasn’t just matching tools or titles — it was identifying functional depth and contextual fit. 3. That nuance — context over credentials — changed how the team viewed talent altogether. Sometimes, the smartest innovation in hiring isn’t about automation. It’s about understanding people through data. That’s what AI Skills Match is doing — giving recruiters X-ray vision to see real job fitment beneath layers of keywords and job titles. #HiringTransformation #DataDrivenHR #RecruitmentReimagined #WorkforceAnalytics #AIForGood #HRTech #TalentAnalytics #SkillsOverDegrees #iMocha

Let's have a look at whats going on behind the scenes. These systems are often built on machine learning models trained on historical hiring data, which is where bias can creep in. 1. Resume Parsing & Ranking (NLP) AI parses CVs and scores them based on: -Keyword matches (“Python,” “Team Management”) -Experience level (inferred from job titles, years) -Education relevance Algos: Logistic Regression, Decision Trees, TF-IDF with NLP models like BERT or GPT for context 2. Predictive Fit Scoring (Supervised Learning) Predicts how likely a candidate is to succeed in a role using: -Skills to role mapping -Similarity to previous “successful” hires Algos: Gradient Boosting, Random Forest, XGBoost, or Deep Learning models 3. Video Interviews & Sentiment Analysis Scoring based on: -Facial expressions -Speech cadence, tone -Vocabulary usage Algos: Computer Vision (CNNs), Audio Analysis, Sentiment Classifiers Many AI hiring tools appear objective but can bake in systemic bias, especially if they rely on biased training data or score based proxies including the likes of dates of graduation = age I have no doubt, AI will bring many advances to Talent & People, but most companies aren't even getting the basics right. If you have a 25% annualised attrition, AI is not going to fix this. #AI #ethics #recruitment #oryxsearch https://lnkd.in/dH_FFiNA

🔄 Large-scale #MachineLearning algorithm + rich administrative #data = improved labour market #matching! 📖 In a nutshell, this is the new study Sabrina Mühlbauer and I have just published in the renowned AI journal «Machine Learning with Applications». We exploit comprehensive #employment biographies from Germany, covering individual characteristics and job-related information, to estimate employment probabilities across occupations. The algorithm generates personalised job recommendations. We demonstrate why machine learning methods are particularly well suited for administrative labour market data and outperform traditional statistical approaches. Random forest consistently achieves the highest predictive performance. It captures nonlinear relationships and complex interactions, remains robust in high-dimensional settings, and reduces overfitting. Compared to conventional models, it better exploits the full informational content of employment histories. The results suggest that ML-based matching, relative to standard statistical approaches, could hypothetically reduce the #unemployment rate by up to 0.3 percentage points. Our goal is to support caseworkers just as job seekers and employers in expanding their job search strategy: the strength of people and data combined. We are working on it. ⚒

𝗧��𝗲 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 𝗧𝗿𝗮𝗽 𝗶𝗻 𝗔𝗜 𝗛𝗶𝗿𝗶𝗻𝗴. 𝗔 𝗣𝗲𝗼𝗽𝗹𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 Your hiring algorithm reports high predictive validity. It was validated on the 20% of candidates it selected.   The rest were screened out before they could generate any performance data. I/O psychologists recognized decades ago what happens when you validate predictors on restricted samples. The validation becomes circular. The algorithm selects candidates, observes that they perform well, and treats this as confirmation. Candidates who might have disproved the prediction never entered the system. The reported validity could be accurate or could be a measurement artifact. No data exists to tell the difference.   Carissa Véliz [𝘗𝘳𝘰𝘱𝘩𝘦𝘤𝘺, 2026] argues that AI predictions about people tend to be self-fulfilling. They create the conditions that confirm them. In hiring, the self-fulfilling mechanism is the validation methodology itself.   Scale compounds the problem. When most large employers use similar algorithms trained on similar data, the same population segments are excluded everywhere. The counterfactual evidence that would challenge the predictions is never generated by any employer.   A PA function that accepts reported predictive validity without examining the validation sample is endorsing an unfalsifiable claim as evidence. The same circularity applies whenever the organization predicts outcomes and then observes only those for the selected population.   When your hiring vendor reports predictive validity, ask what population the validation was conducted on. If the answer is the candidates who were hired, the evidence is circular.   Dave Millner, Nicole Lettich, Abid Hamid, Tilman Sheets, Colby Kennedy Nesbitt, Ph.D., Igor Menezes   #peopleanalytics #predictivehiring #IOpsychology #talentacquisition

🔍 Are Tools Like Pulsifi & Korn Ferry the Magic Solution for Hiring and Talent Decisions? There’s a growing buzz around psychometric and AI-driven platforms in the talent world — and it’s easy to see why. Companies are under pressure to make smarter, faster, and fairer decisions, especially when it comes to hiring and internal growth. Tools like Pulsifi, Korn Ferry, and others promise to take the guesswork out of people decisions by combining personality insights, cognitive ability tests, behavioral data, and predictive algorithms. In theory, it sounds like the perfect formula: ✅ Everyone assessed by the same standard ✅ Easier to manage large applicant pools ✅ Data to predict who might thrive in your culture And yes — when used well, these tools can add a valuable layer of consistency and even help individuals better understand their own strengths and development areas. But let’s not pretend they’re perfect. ❌ They don’t always “get” context — like team dynamics, evolving business needs, or cultural fit. ❌ There’s a risk of leaning too heavily on scores and overlooking real-world experience or growth potential. ❌ Sometimes, top scorers don’t perform — and the quiet gems get missed. 🧠 So how do employers actually use these tools? From what I’ve seen, they usually fall into one of three camps: 1️⃣ To validate a gut feeling — “We already like this person; the results just confirm it.” 2️⃣ For growth — spotting high-potential talent or leadership candidates internally. 3️⃣ As a filter — using scores to screen out applicants early in the process. But the best leaders I know don’t treat these tools as gospel. They treat them as just one part of the bigger picture. 🔧 A Better Way to Use These Tools If you really want to make good people decisions, pair psychometric insights with: • Technical and functional assessments • Structured interviews • Past performance reviews • Peer and team feedback • Simulations, role plays, or real-life challenges That’s how you see the whole person — not just their profile. ✅ A Few Tips for Using These Tools Wisely: 1. Let them guide, not decide. 2. Always interpret results in context — no role exists in a vacuum. 3. Train your hiring managers to read results thoughtfully. 4. Be open with employees — use it for growth, not judgment. 5. Keep an eye out for biases, even when it feels “data-driven.” 6. Align with your broader talent strategy — don’t use tools in isolation. ✨ Final Thought A psychometric test might tell you who someone is today — but not who they could become tomorrow. Let’s remember: hiring and leadership decisions are about people, not just profiles. When we blend data with empathy, experience, and sound judgment, we unlock real potential — not just patterns on a dashboard. Because the best hires aren’t just the ones who test well — they’re the ones who grow well. #PeopleFirst #TalentDevelopment #Psychometrics #LeadershipMatters #HRWithHeart #EmpathyInLeadership

Predictive analytics in HR uses historical data and algorithms to forecast future outcomes, such as predicting the success of candidates, employee retention, and performance. This technology allows HR professionals to make data-driven decisions that enhance recruitment processes, making them more efficient and effective. In a recent application of AI-driven predictive models, we were able to identify high-potential candidates earlier in the hiring process. These models analyze factors such as cultural fit, career progression, and past job performance to predict a candidate's likelihood of success in a specific role. The impact has been significant: 🔹 Faster hiring: We were able to prioritize candidates who were most likely to succeed, reducing the time-to-hire. 🔹 Improved quality of hires: The use of data-driven insights allowed us to bring in candidates who were better suited for the roles and contributed positively to team dynamics. 🔹 Better retention: By predicting which candidates were likely to thrive within the company’s culture, we were able to make more informed hiring decisions that contributed to long-term success. AI is not here to replace HR professionals but to enhance our ability to make smarter, more informed decisions. By leveraging predictive analytics, we can streamline processes and ensure we’re bringing in the right talent, faster. #AIinHR #RecruitmentTech #PredictiveAnalytics #TalentAcquisition #HRInnovation #DataDrivenDecisions #FutureOfWork