How to Highlight Skills in a Tech Portfolio

Your portfolio might be missing these underrated elements. Most people focus on polished case studies and pretty visuals. But what actually makes a recruiter pause and think “I want to talk to this person” are the things you don’t usually see. Here are 4 to start adding. 1️⃣ Show your decision trade-offs Don’t just show the final design. Show the fork in the road. What options did you consider, and why did you choose the one you did? Side-by-side screenshots + a short explanation = proof of your critical thinking. 2️⃣ Highlight collaboration moments Portfolios often read like solo projects, but hiring managers want to see you as a teammate. Call out where a PM, dev, or researcher’s input shifted the outcome. Add a quick “before & after” to show the impact of collaboration. 3️⃣ Call out constraints Great design isn’t created in a vacuum. Were you working under a tight deadline? Legacy tech? Limited resources? Own it. Explain how you adapted your solution within the real-world boundaries. That’s what makes your work practical and credible. 4️⃣ Add a “What I’d do differently” section Reflection shows growth. Wrap up each case study with 2–3 quick bullets: what worked, what you’d approach differently, and what you learned. It signals self-awareness without undermining your work. These details don’t just show your work, they show how you work. Now, let’s turn this into a community resource 👇 If you’ve got a portfolio you’re proud of (or one in progress!), drop it in the comments so we can start building a list for visibility and inspiration!

For folks who use GitHub and are in early stage careers and hope to add GitHub as a value add to your profile - here is a note. If interviewing for an SDE role, GitHub projects that don't solve a problem and are just a coding exercise are not very helpful. This may sound perplexing but it needs to be said. : Hiring managers and tech leads (like me) look for problem-solvers. A repository full of practice exercises might show you can write code, but it doesn’t demonstrate that you can build impactful solutions. ► How to Make Your Projects Stand Out 1. Frame Them as Solutions: Instead of presenting your project as "just another app," position it as a business solution or a tool that solves a real-world problem. For example: - Instead of “Expense Tracker App,” say, “A tool for freelancers to manage and categorize expenses for tax season.” - Instead of “Weather App,” frame it as, “A weather app optimized for agricultural planning with location-based crop suggestions.” 2. Highlight the Problem It Solves: Every great project starts with a problem. Make it clear what problem you identified and how your project addresses it. - Example: “This tool was designed for small business owners who struggle with automating their daily sales tracking.” 3. Show Quantifiable Value: Numbers tell a story. Include metrics like: - How much time/money the solution saves. - How many users it could potentially impact. - Any test data or feedback you’ve collected. - Example: “This app reduced invoice processing time by 35% in a real-world test case.” 4. Document It Well: A project is only as good as its README. Include: - A brief description of the problem it solves. - Key features. - Instructions on how to run/test it. - Screenshots, GIFs, or a demo link to bring it to life. Having a GitHub full of clone apps or unfinished side projects sends the wrong signal. It doesn’t show creativity, ownership, or impact, it shows you can follow tutorials, and that’s not what companies hire for. Instead, invest your time into one or two high-impact projects that: - Solve real-world problems. - Show off your ability to understand user needs. - Demonstrate your thought process, design skills, and technical execution. So, take a step back, revisit your GitHub, and think: - Does this project solve a problem? - Can I explain its value to someone outside of tech? - Would I hire someone based on this work? If the answer isn’t “yes,” it’s time to rethink how you approach your projects. Remember: One impactful project > 100 clones. Focus on impact, not just output.

𝗜 𝗮𝘀𝗸𝗲𝗱 𝗯𝗶𝗴 𝘁𝗲𝗰𝗵 𝗿𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿𝘀 𝘄𝗵𝗮𝘁 𝗽𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗵𝗲𝘆 𝗶𝗻𝘀𝘁𝗮𝗻𝘁𝗹𝘆 𝗶𝗴𝗻𝗼𝗿𝗲. Their answers changed everything. I used to think any project on my resume was better than nothing. So I built a to-do list app, a calculator, a weather tracker—you know, the usual. Then I asked recruiters from Amazon, Meta, and Google what projects they instantly ignore. Their response? “If we’ve seen it 100 times before, we skip right past it.” Here’s what they told me not to put on my resume: ❌ To-do lists ❌ Calculator apps ❌ Basic CRUD apps with no real-world impact ❌ Portfolio websites (unless you’re a designer) ❌ Copy-paste tutorial projects 𝗦𝗼 𝘄𝗵𝗮𝘁 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝘁𝗮𝗻𝗱 𝗼𝘂𝘁? Recruiters want to see projects that show real-world impact, problem-solving, and creativity. ✅ 𝗔𝗻 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗿𝗲𝘀𝘂𝗺𝗲 𝘀𝗰𝗿𝗲𝗲𝗻𝗲𝗿 – A tool that scans job descriptions and suggests resume optimizations. ✅ 𝗔 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝗳𝗼𝗿 𝘀𝗺𝗮𝗹𝗹 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 – Helping them adjust pricing during off-peak hours to boost revenue. ✅ 𝗔 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 – Aggregating user feedback and behavior for product teams. ✅ 𝗔𝗻 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝘁𝗼𝗼𝗹 – Something that saves time or reduces manual effort in a business process. ✅ 𝗔𝗻𝘆𝘁𝗵𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝘀𝗼𝗹𝘃𝗲𝘀 𝗮 𝗿𝗲𝗮𝗹, 𝘁𝗮𝗻𝗴𝗶𝗯𝗹𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 – If a company can see how your project could be useful, you’re already ahead. The best projects aren’t the ones that showcase your coding skills—they’re the ones that showcase your ability to solve real problems. If your portfolio projects aren’t getting you noticed, it’s time to build something that actually matters. What’s the best project you’ve built?

If you want a standout portfolio in 2025 as a beginner Data Scientist or AI Engineer, use this framework👇 1. Select a Meaningful Problem → Choose a real-world issue you're genuinely interested in (e.g., climate change prediction, healthcare improvements, social media analytics) → Clearly define the objective and the potential impact of solving this issue 2. Acquire and Document Data → Use reliable sources (Kaggle, UCI Repository, Hugging Face) → Clearly document your process for selecting and gathering the data 3. Data Preparation → Clean and preprocess the data thoroughly → Outline key steps (handling missing data, normalization, feature engineering) 4. Exploratory Data Analysis (EDA) → Generate visualizations and summary statistics → Clearly state insights and how they guide your modeling decisions 5. Select Appropriate Algorithms → Choose suitable methods (e.g., Transformer models, XGBoost, clustering) → Provide reasoning for your choice based on the problem and data 6. Develop and Optimize Your Model → Write clean, reproducible, and modular code → Clearly document model experimentation, model training, hyperparameter tuning, and validation steps 7. Evaluate Your Model → Use relevant metrics (ROC-AUC, F1-score, RMSE, BLEU, MMLU) → Present your evaluations clearly, including visualizations like ROC curves or confusion matrices 8. Analyze Results Critically Clearly interpret outcomes, discuss strengths, limitations, and biases Suggest realistic improvements and next steps 9. Deploy Your Model (Optional) → Create a simple web app using tools like Streamlit, Hugging Face Spaces, Flask, or FastAPI → Provide a working demo and clearly document its functionality 10. Comprehensive Documentation → Write a professional, detailed README. → Clearly summarize your project's purpose, methodology, results, and real-world relevance 11. Let your work talk → Share the code, data catalog, and documentation to reproduce on GitHub → Write a detailed blog about interesting insights and outcomes from the project, and share it on Substack/ Medium/ LinkedIn article You can use this framework to build as many projects as you like. While doing multiple projects make sure to explore different use-cases and different algorithms, which will help you get a holistic view of the Data & ML space. PS: LinkedIn post has character limit, so I will be sharing a list of portfolio projects I would recommend to start with, in the next post -------- Share this with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights, news, and educational resources to keep you up-to-date about the AI space!

I have reviewed 100+ portfolio projects. If you want employers to hire you even without experience, Make sure your project does these 𝟲 things. A great portfolio isn’t just a collection of skills It’s a showcase of how you solve real problems. This is what makes a portfolio project stand out: => 𝗜𝘁 𝘁𝗲𝗹𝗹𝘀 𝗮 𝘀𝘁𝗼𝗿𝘆 Every strong project follows a simple arc: Problem → Solution → Impact. Make it clear what challenge you tackled, how you solved it, and the results. => 𝗜𝘁 𝘀𝗼𝗹𝘃𝗲𝘀 𝗮 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 The best projects come from real-world problems. Current events: Can you analyze a trending issue? (e.g., election results, COVID trends, mask effectiveness) Daily annoyances: What problem do you wish someone would solve? Do it yourself. => 𝗜𝘁 𝘀𝗵𝗼𝘄𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 Good projects highlight your decision-making and problem-solving. Where did you pivot? What obstacles did you overcome? Show your process. => 𝗣𝗮𝘀𝘀𝗶𝗼𝗻 𝗺𝗲𝗲𝘁𝘀 𝗽𝗿𝗼𝗳𝗶𝘁 The best projects happen where interest meets impact. Find a topic you enjoy, just make sure it’s valuable to potential employers. => 𝗜𝘁 𝘀𝗽𝗲𝗮𝗸𝘀 𝗳𝗼𝗿 𝗶𝘁𝘀𝗲𝗹𝗳 A great project saves you time in interviews. If it’s well-structured, you’ll only need to explain the context once. The results will do the rest. => 𝗜𝘁’𝘀 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 (𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀/𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀) Go beyond basic analysis and build interactive dashboards (Tableau, Power BI, Streamlit). Let your audience explore the data. A good portfolio project isn’t just technical It proves you can solve meaningful problems. Follow me, Jaret André to land the job you want 10x faster.