The job market is brutal. There is no sugar coating it.
Resumes disappear into applicant tracking systems, LinkedIn inboxes go unread, and the uncomfortable truth is that your best shot at landing a role is knowing someone who already trusts you. For students and early-career professionals, that network doesn’t build itself overnight.
So how do you create the conditions where the right people find you before you even need to ask?
It starts with building in public. This is something I talked through with a cohort of Willamette University Master’s of Data Science students recently. But the advice is just as urgent for seasoned professionals trying to stand out in data and AI.
Learning Python, experimenting with models, or analyzing datasets is one thing. But demonstrating that work to employers, or the broader tech community, is something else entirely. Getting creative with public datasets, hobby projects, or non-profit opportunities is how you can sink your teeth into real-world experiences without waiting for an employer to come around and assign it for you.
There are more ways than ever to build a solid data portfolio. Whether you want to specialize in machine learning, analytics, engineering, or data storytelling, plenty of platforms help you share what you’re building and make some noise in the world.
GitHub: Start With the Baseline
For most technical roles, GitHub is still the baseline. It’s the easiest way to show your projects, collaborate with others, and demonstrate how you build things.
A well-structured repository gives people insight into how you think and solve problems. This repository should show:
- The problem you were trying to solve
- Your approach to the data or model
- Clean, readable code
- Documentation someone else can follow
For inspiration, I often point people to strong open-source examples. OpenClaw creator Peter Steinberger has a GitHub that’s worth exploring (and admiring).
Publishing Data Visualizations and Analytics Work
If your focus is analytics or business intelligence, your portfolio should show how you communicate insights—not just build models.
Platforms like these allow you to publish dashboards and visual projects publicly:
These galleries are basically public portfolios for data storytelling. They allow employers, collaborators, and community members to explore your work interactively and see how you translate data into insight.
ML and AI Projects That Actually Stand Out
If you’re focused on machine learning or generative AI, a few platforms are designed specifically for showcasing working models and experiments.
Hugging Face
Hugging Face is the “GitHub for ML.” You can host models and create a front-end (using Gradio or Streamlit) for free. It’s become the industry standard for showcasing NLP, Computer Vision, and Generative AI projects.
DagsHub
DagsHub is often called the “GitHub for Data Scientists.” It integrates Git with DVC (Data Version Control) and MLflow. It allows you to showcase data pipelines and experiment tracking alongside your code.
OpenML
OpenML is a collaborative platform for sharing datasets and machine learning experiments. It’s an excellent place if you want to contribute to open science or benchmark your models against global standards.
If you’re wondering what kinds of projects make a portfolio stand out, Egor Howell does a great job breaking it down in this video, Don’t Build an ML Portfolio Without These Projects.
He highlights three types of projects that tend to attract attention from recruiters:
- 3–5 foundational projects that demonstrate different algorithms and datasets
- An end-to-end ML system that includes data ingestion, training, and deployment
- A research-focused project that explores or reimplements a research paper
Employers want to see that you can build, ship, and communicate in the real world.
Write About What You’re Learning
Last, but not least, is writing.
My Schema Sauce blog has served me well over my career, and I hope a blog serves yours!
A powerful way to stand out is simply to write about what you’re building or learning. You don’t need groundbreaking research to start publishing. Explaining concepts, documenting projects, or reflecting on experiments can demonstrate curiosity and communication skills. With AI, you have an editor and publisher in your pocket.
Platforms like WordPress, Substack, or LinkedIn make it easy to share your work and experience. WordPress is more involved, but you own your content. Substack and LinkedIn are built for ease with features like newsletters that make it easy to keep your audience engaged.
Even simple portfolio sites built with tools like Canva can do the job. Here is a past Master’s student’s portfolio built with Canva, which helped her land her first job.
The goal isn’t to try to be a superstar writer. Just show how you think, and it will get you noticed.
Keep Building and Keep Sharing
Building amazing things is important. Making them visible is what gets you opportunities.
A strong portfolio shows how you think, how you solve problems, and how you communicate your work. Whether that’s through GitHub, dashboards, ML experiments, or writing, the key is to keep building—and keep sharing.
If you have suggestions on portfolio options I missed, shoot me a note. I would love to hear from you and continuously update these suggestions.
