Data Science Society: Head of Project Labs

You shouldn't need a perfect technical background to build an incredible data science project. My vision for Project Labs is simple: an accessible space where anyone can build something exceptional. As a current mentor in Project Labs, who guided teams from their first ideas to polished showcases, I know exactly how to make that happen.

I am running for Head to elevate our curriculum and expand our industry presence. Running my own startup taught me how to lead with resourcefulness, while serving as an elected representative for over 2,000 students at school taught me how to deliver real value. In that role, I worked directly with external companies to open up exclusive internships and workshops. I know how to run these labs, and I know how to scale them.

If elected, my plan is:
Interactive and Advanced Workshops: I will transform passive lectures into highly interactive sessions. We will dive past the basics into advanced concepts, like building machine learning models from the ground up, so you truly grasp the logic behind the code.

Overcoming Technical Hurdles: Having mentored teams through the full development lifecycle, I have seen exactly where projects stall. I will streamline our technical support to ensure everyone finishes the year with a career-ready project. 

Bigger Partners, Better Prizes: I will leverage my outreach experience to secure top-tier industry sponsors, bringing in expert judges, better showcase prizes, and direct internship pipelines.

Data science is most powerful when it's hands-on. But too often, project-based learning feels either overwhelming for beginners or unstimulating for those further along. I want to change that.
As Head of Project Labs, I'll design projects that adapt to where members actually are — not where we assume they should be. That means structured pathways where participants build confidence early, then stretch further as they progress, so no one feels left behind, and no one feels unchallenged.
I also believe learning sticks when it's enjoyable. I'll bring energy and creativity to how projects are run — making sessions feel collaborative and exciting rather than like another tutorial to sit through.
My background gives me a strong foundation for this. I've built end-to-end ML projects independently, including a computer vision model predicting London neighbourhood deprivation using Street View imagery, and I've competed in multiple hackathons where I've had to learn fast, adapt, and deliver under pressure. I know what it feels like to be thrown into the deep end — and I know how to make that experience productive rather than demoralising.
I'm a first-year, and I'm newer to DSS than some candidates. But I bring genuine technical depth, a real passion for making data science accessible, and the drive to make Project Labs something members genuinely look forward to.
Let's build something great together.

I am Huiru Feng, a first-year Pure Math student. Starting with zero coding background post-high school, I built my tech stack from scratch: mastering Python (Coursera), Pandas & Data Cleaning (Kaggle), ML/Deep Learning, and Forage industry projects. Currently, I lead a DSS team building an EV vs. Legacy predictive model.

Having navigated the steep curve from complete beginner to project lead, I understand members' exact pain points. I am standing for Head of Project Labs to provide scientific guidance and fix two critical bugs: uneven contributions and high technical barriers.

My Execution Plan:

• 1. Anti-Freerider Framework: Dedicated members shouldn't carry the team. I will implement strict milestone tracking and role delegation (Data Engineer, Modeler, Analyst). Everyone will have clear, trackable deliverables.
• 2. AI-Assisted Workflows: Actively tracking real-time AI trends, I know tedious data prep kills momentum. I’ll introduce "AI-Driven Workflows" with Kaggle-style templates and AI coding plugins, scientifically lowering the barrier so beginners can focus on core model architecture.
• 3. Tiered Tracks: Projects will be categorized by difficulty—from guided beginner EDA to Algorithmic Trading and Deep Learning—suiting every member's exact level.

Why Me? With a First-Class Analysis foundation (89%) and an execution-driven mindset, I will use structured management to bridge the experience gap. Less friction, no freeriding, more resume-ready models.