Candidate Evaluation Methods

Most portfolios fail in the first 10 seconds. Here’s why: I'll tell you exactly when I know a portfolio won't make it past my screen. The moment I land on "Hi, I'm a passionate designer who loves solving problems..." Listen. I've already read your CV. I know your name, your experience, and where you're based. I don't need a repeat performance. What do I need? To see if you can actually design. Here's what happens when I review portfolios: I have 10 seconds to decide if your work is worth 5 minutes of my additional review and hours of the interview process. And you're wasting those seconds telling me you "love design." Of course, you love design. You're a designer. That's expected. Show me this instead: → Your work / style / taste (Immediately) → The problems you've solved → The impact you've created → Your actual design thinking When I land on your portfolio, I'm looking for: First impressions that matter. Is it accessible? Any animations that show craft? Does it load fast? Can I navigate intuitively? Your portfolio IS the first design problem I see you solve. And if you can't design for me, your user, why would I trust you with my users? What actually gets you hired: ✓ Business context as a stage setting ✓ Your specific role (not "I did everything") ✓ Team composition and timeline ✓ The REAL problem you solved Not 20 personas. Not 50 wireframes. Not your entire design process is outlined. Give me: - 2-3 key research insights - 1 example of iteration that mattered - The final solution (3 screens max) - Actual impact or expected metrics Here's the brutal truth: I don't care about your design philosophy. I care if you can move my metrics. Design isn't just about beauty or experience. It's about business impact. Show me you understand that balance: - Skip the autobiography. Start with your best work. - Make me think "I need to talk to this person". Not "I need to read more about them." Your portfolio should work like your best designs: Clear. Intuitive. Impactful. Remember: I've hired dozens of designers. The ones who got offers? They showed me their thinking through their work. Not through their "About Me". Designers, what's the first thing visitors see on your portfolio? Time for some honest self-assessment (and a potential change).

I’ve reviewed > 400 portfolios this year. Observation #1: The ones that got interviews weren’t the prettiest. They were the clearest. → Clear intent (what roles they’re targeting) → Clear structure (who they helped + what changed) → Clear thinking (how they made decisions) Observation #2: Hiring managers responded best to portfolios that made it easy to scan, not admire. → 3-5 second headlines that told the story → Metrics up top, visuals in the middle, lessons at the end → Less storytelling. More signal. Observation #3: The portfolios that ‘failed’? → Opened with “Hi, I’m Alex and I love solving problems” → Contained 30+ screenshots with no explanation → Didn’t articulate business impact or their role → Had no opinion, no POV, no process If I were applying today? → I’d restructure my case studies to lead with outcomes → I’d add a design philosophy section to show how I think → I’d cut 40% of the fluff and focus on what actually matters → I’d communicate my USP and elevator pitch up front Your portfolio isn’t a gallery. It’s a business case for why you’re worth hiring. ----- Just thought I'd share this after reviewing some notes over the weekend. Hope it helps! ----- #ux #tech #design #ai #business #careers

Evaluation is the key to successful model development! Reward Models and LLM as a Judge are often used as replacements for human evaluation but require costly preference data! Meta tries to solve this using an iterative self-improvement method and synthetic data generation to improve LLM Evaluators without human annotations. With this method, they improved Llama3-70B Instruct on RewardBench by 13%. 👀 Implementation 1️⃣ Collect a dataset of instructions covering various topics and complexities. 2️⃣ Prompt LLM to generate two responses, 1x high-quality response and 1x intentionally sub-optimal response (e.g., by introducing errors or omitting critical information). 3️⃣ Use the model as an LLM to generate reasoning traces and judgments for these pairs. 4️⃣ Train the LLM on the synthetic preference data, including reasoning and final judgments. 5️⃣ Use the improved LLM evaluator to generate better judgments on the synthetic data. 6️⃣ Retrain the LLM evaluator with these self-improved judgments. Repeat Steps 2-6 using the previous Evaluator for generation, judgments, and then training. 🔄 Insights 📈 Improved Llama 3 70B on RewardBench from 75.4% to 88.3%. 🤖 Achieved comparable results as models trained on human-labeled data. 🔧 Synthetic approach allows generating Evaluators based on custom criteria, e.g. always include citations. 🔄 Iterative approach leads to incremental performance gains. 🚨 Initial LLM biases might be amplified during the iterative approach. Paper: https://lnkd.in/eaMBHPmy Github: https://lnkd.in/eb7zNJsd Models: https://lnkd.in/e-XKD83X Dataset: https://lnkd.in/et5R4qWV

Many teams overlook critical data issues and, in turn, waste precious time tweaking hyper-parameters and adjusting model architectures that don't address the root cause. Hidden problems within datasets are often the silent saboteurs, undermining model performance. To counter these inefficiencies, a systematic data-centric approach is needed. By systematically identifying quality issues, you can shift from guessing what's wrong with your data to taking informed, strategic actions. Creating a continuous feedback loop between your dataset and your model performance allows you to spend more time analyzing your data. This proactive approach helps detect and correct problems before they escalate into significant model failures. Here's a comprehensive four-step data quality feedback loop that you can adopt: Step One: Understand Your Model's Struggles Start by identifying where your model encounters challenges. Focus on hard samples in your dataset that consistently lead to errors. Step Two: Interpret Evaluation Results Analyze your evaluation results to discover patterns in errors and weaknesses in model performance. This step is vital for understanding where model improvement is most needed. Step Three: Identify Data Quality Issues Examine your data closely for quality issues such as labeling errors, class imbalances, and other biases influencing model performance. Step Four: Enhance Your Dataset Based on the insights gained from your exploration, begin cleaning, correcting, and enhancing your dataset. This improvement process is crucial for refining your model's accuracy and reliability. Further Learning: Dive Deeper into Data-Centric AI For those eager to delve deeper into this systematic approach, my Coursera course offers an opportunity to get hands-on with data-centric visual AI. You can audit the course for free and learn my process for building and curating better datasets. There's a link in the comments below—check it out and start transforming your data evaluation and improvement processes today. By adopting these steps and focusing on data quality, you can unlock your models' full potential and ensure they perform at their best. Remember, your model's power rests not just in its architecture but also in the quality of the data it learns from. #data #deeplearning #computervision #artificialintelligence

After 16+ years of working in tech and interviewing 500+ candidates, I can say that the most technically skilled candidate often doesn’t get the job. In fact, I’ve seen the most technically brilliant person in the room lose the offer, more than once. Because once you’ve proven you can do the work, the question changes. The panel stops asking, “Can they code/design/ship? And starts asking: Do we actually want to work with this person every day? I’ve seen candidates talk down to interviewers, and brilliant minds fail to explain their ideas clearly. Every time, they didn’t get the offer. And then someone slightly less technical came in who was collaborative, clear, and easy to work with, and got the job. So here's what you should do to stand out. 1. Explain things simply If interviewers can’t follow your thinking, they won’t trust you to communicate in a team. Practice explaining your ideas as if you were talking to a smart friend outside your field. 2. Share credit, not just results Talk about how you worked with the designers, QAs, and the PMs. That signals you know how to play as part of a team. 3. Stay humble Panels don’t want a know-it-all. The best candidates say things like, “There are a couple of approaches here, and here’s how I’d weigh the trade-offs.” That shows maturity and openness, two traits teams trust. 4. Don’t underestimate likability This one decides more offers than you’d think. In debriefs, I’ve heard panels say, “I don’t know if they were the strongest technically, but I’d love to work with them.” This is the reality of hiring in modern product organizations. Competence gets you considered, but likability, communication, collaboration, and trust decide if you’re chosen. Repost this if it resonated. P.S. Follow me if you are a tech job seeker in the U.S. or Canada. I share real stories and proven strategies to help you land interviews at the top companies.

When I was starting out in cybersecurity, one thing that gave me an edge was doing practical projects I could proudly talk about. That’s why I always share this with beginners: You don’t need to wait for your first job to build experience. Start with job simulations. There’s a platform called Forage where you can do free cybersecurity job simulations from real companies like Mastercard, AIG, and Datacom. These aren’t just theory you’ll get to solve real problems and add them as projects on your resume or LinkedIn. Here are 4 I recommend (100% free and beginner-friendly): 1. Datacom Cyberattack Investigation & Risk Assessment Investigate a simulated cyberattack and perform a risk assessment. Link: https://lnkd.in/dsfz9aTd 2. Mastercard Cybersecurity Awareness Team Join Mastercard’s awareness team to identify and reduce cyber risks. Link: https://lnkd.in/dD-cWPY7 3. Tata Group IAM Developer Simulation Support a consulting team and improve identity & access management. Link: https://lnkd.in/dZjndnAA 4. AIG Ransomware Attack Response Respond to a ransomware attack using security alerts and basic Python. Link: https://lnkd.in/dVDnKKYd These helped me, and I hope they help you too. You can start building real skills today no job title required. Which one will you try first? #CybersecurityCareer #BeginnerCybersecurity #JobSimulations #Forage #Cybertalkswithjojo

92% of HR leaders believe that soft skills are crucial, even more so than technical skills. At Supersourcing, we’ve observed firsthand that teams with strong, soft skills consistently outperform purely technical teams by 25%. So, how do we evaluate these crucial skills? At Supersourcing, we follow a simple approach: - We use a set of questions on a Google Form to pose questions that reveal candidates’ thought process and understanding, beyond just their resumes. This helps us assess their problem solving skills and the knowledge of our organization. - Out first interviews include behavioral and situational judgment tests to see how candidates manage stress, delegate tasks, and communicate under pressure. - We focus on understanding how quickly candidates adapt to new processes, learn new tech, and collaborate with their teams.   The result? Our clients report a 30% increase in project success rates and a 40% improvement in team collaboration. The bottom line is that integrating soft skills evaluation into your tech hiring isn't just nice-to-have—it's essential for building high-performing teams. At Supersourcing, we've built a pool of 60,000+ developers who excel in both technical and soft skills. It's not just about finding someone who can code; it's about finding someone who can communicate, collaborate, and drive projects forward. What soft skills do you prioritize in your hiring process? Share your thoughts below—let's learn from each other and build stronger, more effective tech teams!

Would you buy a car without a test drive? I feel "hiring someone" is a much more important decision than buying a car- both for the employer and employee and yet most companies have a really superficial hiring process. Interviews cannot actually replace how the person actually works in a real environment. And in the last 6-7 years- I have made a lot of bad hiring decisions. Whats has changed now?- We try hiring someone for a mini-project before actually hiring them. (we pay for them for this project) It is usually less than a week, 2-3 days at max. It is as useful an exercise for us as it is for them. We can see how they think, how they make decisions, the kind of questions they ask, how easy it is to explain something to them and we can actually see how their work is instead of believing their words/ resumes and case studies. Some companies even make simulated environments or fake projects for this- 1. In a factory in South Carolina, BMW built a simulated assembly line where job candidates get ninety minutes to perform a variety of work-related tasks. 2. Cessna, the airplane manufacturer, has a role-playing exercise for prospective managers that simulates the day of an executive. Candidates work through memos, deal with (phony) irate customers, and handle other prob-lems. Cessna has hired more than a hundred people using this simulation. A lot of companies have realized that when you get into a real work environment, the truth comes out. "It's one thing to look at a portfolio, read a resumé, or conduct an interview. It's another to actually work with someone " (Rework by Jason Fried and David Hansson) #hiring #employees #jobs

You’re one panel interview away from landing your dream job. Here’s how you can succeed. I know, panel interviews can feel overwhelming, too many faces, too many questions, and way too much pressure. But with the right approach, you can take control of the room and leave a lasting impression. Here’s how to prepare like a pro: 1. Know who’s in the room Ask for the panelists' names and roles ahead of time. Research what matters to each of them so you can speak to their priorities, not just the job description. 2. Prep flexible, high-impact stories Choose examples that showcase leadership, cross-functional collaboration, and results. The best stories can be adapted across different types of questions. 3. Engage the whole panel Even if only one person asks the question, speak to the room. Make eye contact with everyone. Confidence is contagious and visible. 4. Tailor your questions Show you’ve done homework by asking thoughtful, strategic questions based on each person’s perspective. It signals you’re already thinking like a peer. 5. Control the energy Panel interviews can feel intense. Slow your pace, stay grounded, and remember, you're not there to impress everyone. You're there to connect. This is your opportunity to demonstrate presence, clarity, and leadership under pressure. You don’t need to have all the answers. You just need to show up like someone who belongs in the room. What’s the one part of a panel interview that throws you off the most? Drop it in the comments. Let’s break it down.

New Role Play on "How to Emotionally Connect with an Interviewer"! I’m excited to announce I’ve built my first role-play scenario with LinkedIn Learning’s AI-powered coaching, specifically for people currently in the process of interviewing! Role play allows you to practice real-world conversations, using AI, to help you develop critical human skills through realistic conversations. In this scenario, you’re being interviewed for a job in the industry you’ve been trying to break into. You really want to make a good impression, and know how important it is to emotionally connect with an interviewer. Here's the link to the practice scenario: https://lnkd.in/ggM4Pp9a What to Expect: You can practice role-play scenarios using text or voice. After each conversation, you will receive instant, actionable feedback. Why I Recommend It: Like many other professionals seeking a job, if you’ve ever struggled with nailing an interview in a way that really makes you stand out, then this safe and realistic conversation experience will help you practice emotionally connecting with the interviewer. I can tell you from my own experience in interviewing thousands of candidates as a team lead recruiter for Procter & Gamble, connecting emotionally comes down to showing up as authentic, confident, and enthusiastic. This scenario helps you practice all this! It’s live now—and completely FREE for you to try it. After you’ve practiced the scenario I’d love to hear what you think in the comments below  . #AIRolePlay #LinkedInLearning #LinkedInLearningInstructor #AICoaching