Candidate Evaluation Methods

I Can Spot a Great Candidate in 30 Seconds - Without Looking at Their Resume. At Vicco Laboratories, the first few interview rounds are handled by our HR and leadership team. They assess skills, experience, performance history - all the standard checkboxes. But when someone reaches my room, I’m not evaluating capability. I’m evaluating character. Because skills can be trained. Character can’t. So in the final round, I deliberately observe three things before we even get into formal questions: 𝐓𝐫𝐚𝐢𝐭 1: 𝐇𝐨𝐰 𝐓𝐡𝐞𝐲 𝐓𝐫𝐞𝐚𝐭 𝐭𝐡𝐞 𝐒𝐦𝐚𝐥𝐥𝐞𝐬𝐭 𝐏𝐞𝐫𝐬𝐨𝐧 𝐢𝐧 𝐭𝐡𝐞 𝐑𝐨𝐨𝐦 Before they enter, I always ask our receptionist to make them wait for a few minutes. Not to trouble them — but to observe: Do they greet her or ignore her? Do they show gratitude or entitlement? Do they smile or stay blank? Do they thank her when being called in? If someone is only respectful upwards, they’re not fit for leadership. 𝐓𝐫𝐚𝐢𝐭 2: 𝐇𝐨𝐰 𝐓𝐡𝐞𝐲 𝐇𝐚𝐧𝐝𝐥𝐞 𝐒𝐢𝐥𝐞𝐧𝐜𝐞 During the conversation, I pause intentionally. A great candidate: Doesn’t panic when things go quiet Holds eye contact without overcompensating Thinks before responding, instead of rushing to impress Silence is a pressure test.  Silence exposes a person’s comfort with themselves. And self-assured people make better decisions under pressure. 𝐓𝐫𝐚𝐢𝐭 3: 𝐖𝐡𝐞𝐭𝐡𝐞𝐫 𝐓𝐡𝐞𝐲 𝐀𝐬𝐤 “𝐖𝐡𝐚𝐭 𝐂𝐚𝐧 𝐈 𝐆𝐢𝐯𝐞 𝐭𝐨 𝐕𝐢𝐜𝐜𝐨”, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 “𝐖𝐡𝐚𝐭 𝐖𝐢𝐥𝐥 𝐈 𝐆𝐞𝐭?” I watch closely when compensation and responsibilities are discussed. If the questions are only about salary, perks and timings, they’re employees. If they ask about learning culture, values, decision-making structure…they are already thinking as an owner. I’ll always choose alignment over achievement. So if you’re ever preparing for your final round anywhere — don’t just prepare your resume. Prepare your presence. Because long after your words fade, your character stays in the room. Sanjeev Pendharkar  Just sharing what I’ve learnt #values #business #hiring #hr #decisionmaking #cv #leadership #skills

Fundamental concepts every Frontend Engineer should know: (They don't really change, even with AI) 1️⃣ Beginner → HTML: semantic HTML and the DOM → CSS fundamentals: cascade, specificity, layout (box model, flex, grid) → JavaScript: common data types, timeouts, async/await, promises → Forms: how to build forms w and w/o JavaScript → Responsive design, CSS media queries → Networking: HTTP, caching, CDNs, HTTP/2, latency mitigation, and cache invalidation strategies 2️⃣ Intermediate → Accessibility: keyboard first, ARIA, screen readers, contrast, focus management → State management: App patterns and predictable component architecture → Component design: composition, props/inputs, pure components, separation of concerns → Media optimization: formats, responsive images, decoding, lazy loading → Browser basics: rendering engines, repaint vs reflow, layout thrashing → Rendering strategies: CSR, SSR, SSG, etc → Fonts: loading strategies, fallback stacks, and layout shift considerations → Testing strategies: unit, integration, visual, and end-to-end tests, plus testability practices → Deployment basics: static hosting, edge vs origin, cache lifetimes, invalidation recipes 3️⃣ Advanced → Build systems and module formats: bundlers, tree-shaking, code-splitting → Security basics: XSS, CSRF, CSP, secure headers and safe handling of user input → Privacy and permissions: cookies, storage, same-site rules, and browser permission prompts → Offline first-patterns: service workers, PWAs, and indexedDB basics → Internationalization: i18n / l10n approaches, pluralization, date/number formats → Maintainable CSS: utility vs component styles, BEM, CSS-in-JS tradeoffs, atomic CSS → Performance: Critical rendering path and performance optimization, including paint, layout, and compositing → Design systems: Building and maintaining design systems and UI components for the web 4️⃣ Expert → Local-first systems: Offline-parity, CRDTs, and optimistic replication → Front end tooling: Monorepo orchestration, AST transformations, and custom build pipelines → Server-driven UI: JSON-defined layouts, dynamic orchestration, and cross-platform consistency → Microfrontends: Decoupled deployments, module federation, and runtime integration → WebGL / WebGPU: Shaders, hardware acceleration, and high-performance rasterization → WASM: Polyglot execution, near-native performance, and memory management → Browser internals: V8 optimization, event loop deep-dives, and rendering engine mechanics. What else would you add? ——— ♻ Repost to help others discover 📕 Save the post so you don't miss it 💡 Follow me Yangshun Tay and my company GreatFrontEnd for more

A candidate called me on a Friday afternoon. He’d just been rejected. Final stage. No feedback. Just a one-line email from HR. “We’ve decided to move in a different direction.” Six weeks of his life. Three interviews. A presentation he’d spent a weekend building. One line. I called the hiring manager. Asked what happened. “He was great. Just not quite right in the end.” Not quite right. After six weeks and three interviews you didn’t know that? Here’s what that one-line rejection actually cost: That candidate is a packaging sales professional with a 12-year track record. He knows every account in his territory. He’ll be placed somewhere in the next three weeks. He’ll remember that email for the rest of his career. The packaging sector has maybe 200 serious sales hires a year. The candidate you reject today is the reference, the customer, the future colleague of someone you need tomorrow. How you treat people who don’t get the job defines your reputation more than how you treat people who do. One line is not good enough.

Your "perfect candidate" is your biggest mistake. Here's what actually works in 2025: This office sign changed how I hire forever. It said: "Hire for Attitude Train for Hard Skills Mentor for Soft Skills." ↳ Most people nodded. ↳ A few truly understood it. Until I started proving it was true. Example: Sarah, my most controversial hire. ↳ Zero industry experience ↳ Wrong degree ↳ "Too junior" for the role But she had something different: When everyone saw processes ↳ she saw possibilities When we discussed problems ↳ she explored patterns When others defended tradition ↳ she asked "what if?" 6 months later: She rebuilt our entire client approach. ↳ Revenue up 47%. 12 months later: She's training the "experienced" team. Here's what most miss about hiring: Skills are temporary. Mindset is transformative. ↳ I can teach you our tech stack in 3 months ↳ I can't teach curiosity in 3 years ↳ I can train process, but not persistence ↳ I can mentor management, but not drive The game has changed: Your "perfect candidate" with 10 years XP? They're perfect for the past. You need someone who can create the future. After 370 interviews and 15 mis-hires, this is the formula I trust: 50% - Attitude ↳ Learning velocity ↳ Problem-solving instinct ↳ The energy they bring to the room 30% - Potential ↳ Pattern recognition ↳ Adaptability ↳ Leadership instincts 20% - Base Skills ↳ Just enough to start ↳ Everything else can be taught The results? ↳ 90% of these hires exceed expectations ↳ 70% promoted within 18 months ↳ 85% have led major innovations Traditional hiring is broken. ↳ It optimises for the past. ↳ This approach builds the future. Who's the best "inexperienced" hire you've ever made? Share their story below ⬇️ ---- Follow ⚡️Harvey Lee ⚡️ for career acceleration tips that actually work in 2025.

Today, I witnessed my team lead interviewing a Full-Stack Web Developer with 2 years of experience, and I observed some interesting insights. The interview started with basic technical questions, which the candidate answered well. Then, my team lead gave him a coding task: 👉 "Write a function that removes duplicate values from a nested array while preserving its structure (PHP)." The candidate struggled and couldn’t solve it. So, my lead gave him another task: 👉 "Write a function that groups an array of associative arrays based on a specific key." Again, he couldn’t solve it. At this point, he admitted: "I don’t always have syntax and logic rules memorized, as I usually rely on resources like Stack Overflow and ChatGPT. Could you assign me a real-world task instead?" Here,I decided to step in. Since I was working on WooCommerce project, I gave him this challenge: 💡 "Implement a functionality where a logged-in user gets a 15% discount if they place two orders in a single day." We allowed him to use Google, ChatGPT, or any other resources. To our surprise, within 1.5 hour, he understood WooCommerce functionality, analyzed the existing code, and successfully implemented the feature. ✨ We ended up hiring him! ✨ Key Takeaway Many traditional interview techniques overemphasize logical puzzles and syntax-heavy questions, yet modern developers approach problem-solving differently. Rather than memorizing solutions, they adapt, learn, and implement on the spot using the tools and resources available to them. 💡 Instead of testing how well candidates remember syntax, we should focus on how effectively they apply problem-solving skills to practical challenges. What’s your take on this? Let’s discuss in the comments! 👇 #HiringTechTalent #TechCommunity #WhatDoYouThink

I can tell in 5 minutes during an interview if someone understands data (based on how they describe past projects they worked on) Most data candidates that I've interviewed focus on the wrong details entirely during this interview stage and this is definitvely and area that is easy to prepare for. They spend 10 minutes walking through their tech stack: "We used Snowflake with dbt, Fivetran for ingestion, and Airflow for orchestration." But they never explain why those choices mattered, what trade-offs they considered, or what business problem they actually solved. The strongest candidates I interview do the opposite. They start with the business context: "Our sales team was making decisions based on 3-day-old data because our reporting pipeline took 72 hours to run." Then they explain their approach: "We had to balance speed versus cost, so we implemented incremental loading but kept full refreshes for critical accuracy checks." What I'm actually listening for: Do you understand the business impact of your technical decisions? Can you articulate trade-offs you made and why? Do you think about data quality, reliability, and maintainability, not just getting it to work? Can you explain complex systems in simple terms? The candidates who get hired don't have the most impressive projects - they have the clearest thinking about data problems. They talk about monitoring, error handling, and edge cases. They mention stakeholder feedback and iteration cycles. They understand that great data engineering is invisible to end users. Next time you're asked about a past project, spend less time on the tech stack and more time on the problems you solved, decisions you made, and lessons you learned. That's what reveals your actual engineering judgment. What's your experience with this? Share in the comments, follow for more insights on data engineering careers, and ♻️ repost if your network could benefit! #DataEngineering #Interviews #CareerAdvice #TechCareers #JobSearch

How to Fail in a Project Manager Interview Role: Project Manager Topic: Project Management 👔 Interviewer: “How do you ensure a project is successful?” 🧑 Candidate: “By delivering it on time and within budget.” 👔 Interviewer: “Let’s add complexity. Imagine this: The project was delivered on time and within budget, but the client is dissatisfied, claiming the project didn’t meet their business needs. What would you do in such a scenario?” 🧑 Candidate: “I’d explain that we met the agreed project plan and scope.” What the Project Manager Should Have Answered: 1. Deliver Business Value: “Time and budget are essential, but success also depends on delivering business value and satisfying stakeholders' needs. I would involve stakeholders early to define success criteria beyond the project plan.” 2. Manage Scope with Flexibility: “I’d focus on continuous scope validation by conducting regular reviews and adjusting deliverables to align with evolving business goals.” 3. Strengthen Stakeholder Engagement: “I’d ensure consistent communication with stakeholders, keeping them updated on progress, risks, and changes to manage expectations effectively.” 4. Focus on Results, Not Just Deliverables: “Success means achieving measurable business outcomes, like increased efficiency, revenue growth, or customer satisfaction — not just ticking off project tasks.” Impact of the Right Answer: ✅ Value-driven mindset: Prioritizes outcomes over mere project delivery. ✅ Proactive risk management: Prevents misalignment through continuous engagement. ✅ Stakeholder trust: Builds confidence through transparent communication. Takeaway for PMs: Project management isn’t just about finishing tasks. It’s about driving results, managing complexity, and delivering business impact. How do you define project success? Let’s discuss! 💬👇 Need expert insights? #ProjectManagement #PMInterviews #CareerTips #Leadership #ProjectSuccess #StakeholderManagement #PMBestPractices #JobSearch

You applied to 100+ jobs but no interviews? Here's what's actually happening. Your experience is valuable. You're just invisible. Let me explain why, and how to fix it. When you apply online, your resume goes into a database called an ATS (Applicant Tracking System). Think of it like a massive filing cabinet. Now here's the key: Some recruiters don't read every resume. They search. Just like you search Google, they search their database: "Python AND data analysis" "SAFe AND agile transformation" "Tableau AND dashboard" If your resume doesn't have their exact search terms, you’re making it harder to get discovered. You're not rejected. You're just not found. But here's the secret: The job description often tells you EXACTLY what keywords they'll search for. It's like having the answer key. Example from a real job posting: If they say "Experience with Snowflake required"... → They'll search "Snowflake" → Make sure you write "Built data warehouse in Snowflake…" Not "cloud database" or "modern data platform." Use their exact words: Snowflake. I've mapped out 80 keywords that get candidates noticed in 2025: Top searches happening right now: • Python, TensorFlow, LangChain (AI roles) • Kubernetes, Terraform, Docker (tech leadership) • Power BI, Tableau, SQL (data leadership) • SAFe, Agile, DevOps (transformation roles) Your action plan: 1. Read the job description carefully 2. Circle every tool, platform, or methodology mentioned 3. Add those EXACT terms to your resume (if you have that experience) 4. Use them naturally in your accomplishments Example: Instead of: "Led team through digital modernization" You say: "Led SAFe agile transformation using ServiceNow and Jira, reducing delivery time by 40%" You have the experience. Now make it searchable. Your next role isn't rejecting you. It just hasn't found you yet. You’ve got this! 💡 Save this cheat sheet of 80 searchable keywords ♻️ Share to help someone in your network Follow me for more insider recruiting insights

If you use LLM-as-judge, this one is worth reading. (bookmark it) It's actually one of the most effective ways to use LLM-as-a-Judge for evals. Holistic judge scores hide both their reasoning and their ceiling effects. BINEVAL decomposes each evaluation criterion into atomic yes-or-no questions, answers each independently per output, then aggregates the verdicts into calibrated multi-dimensional scores. Every question-level verdict is inspectable, so you can diagnose exactly why an output scored low, and the same verdicts feed straight back as targeted prompt-improvement signal. Across SummEval, Topical-Chat, and QAGS, it matches or beats UniEval and G-Eval, training-free, with especially strong results on factual consistency.

Explaining the Evaluation method LLM-as-a-Judge (LLMaaJ). Token-based metrics like BLEU or ROUGE are still useful for structured tasks like translation or summarization. But for open-ended answers, RAG copilots, or complex enterprise prompts, they often miss the bigger picture. That’s where LLMaaJ changes the game. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗶𝘁? You use a powerful LLM as an evaluator, not a generator. It’s given: - The original question - The generated answer - And the retrieved context or gold answer 𝗧𝗵𝗲𝗻 𝗶𝘁 𝗮𝘀𝘀𝗲𝘀𝘀𝗲𝘀: ✅ Faithfulness to the source ✅ Factual accuracy ✅ Semantic alignment—even if phrased differently 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: LLMaaJ captures what traditional metrics can’t. It understands paraphrasing. It flags hallucinations. It mirrors human judgment, which is critical when deploying GenAI systems in the enterprise. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗟𝗟𝗠𝗮𝗮𝗝-𝗯𝗮𝘀𝗲𝗱 𝗺𝗲𝘁𝗿𝗶𝗰𝘀: - Answer correctness - Answer faithfulness - Coherence, tone, and even reasoning quality 📌 If you’re building enterprise-grade copilots or RAG workflows, LLMaaJ is how you scale QA beyond manual reviews. To put LLMaaJ into practice, check out EvalAssist; a new tool from IBM Research. It offers a web-based UI to streamline LLM evaluations: - Refine your criteria iteratively using Unitxt - Generate structured evaluations - Export as Jupyter notebooks to scale effortlessly A powerful way to bring LLM-as-a-Judge into your QA stack. - Get Started guide: https://lnkd.in/g4QP3-Ue - Demo Site: https://lnkd.in/gUSrV65s - Github Repo: https://lnkd.in/gPVEQRtv - Whitepapers: https://lnkd.in/gnHi6SeW