Head of Data Science

Head of Data Science

Flexible / Hybrid | Early-Stage | High Impact

The Future of Fashion Discovery

As a female-founded startup at an exciting inflection point, we're shaping something genuinely game-changing. This isn't just a product. It's a movement. And we're looking for a brilliant Head of Data Science to help lead the charge.

You'll own and build the intelligence at the heart of the platform — personally designing, building, deploying and iterating on production AI systems while shaping long-term data and AI strategy.

You'll work shoulder-to-shoulder with founders, product and engineering to decide:

• What to build
• What not to build
• When "good enough" is the right answer

What You'll Own

Hands-on AI & Data Leadership

• Personally design, build and deploy production computer vision and agentic AI systems powering search, discovery, recommendations and personalisation
• Own the full lifecycle: problem framing → data exploration → modelling → evaluation → deployment → monitoring → iteration
• Make pragmatic trade-offs between speed, quality and technical elegance

Product & User Impact

• Translate messy user problems into clear, testable interventions
• Partner deeply with Product to optimise for trust, confidence and discovery — not just offline metrics
• Focus relentlessly on feature value and ROI

Data Foundations

• Work hands-on with imperfect datasets
• Design annotation strategies, quality checks and evaluation frameworks from scratch
• Decide where data investment matters — and where it doesn't (yet)

Technical Direction & MLOps

• Establish pragmatic MLOps practices (CI/CD, deployment, monitoring, alerting)
• Build scalable but lightweight pipelines (AWS)
• Ensure models are robust, reliable, explainable where needed and safe in production

Team & Culture

• Set a strong technical and ethical bar for data science
• Mentor future hires as the team grows
• Model curiosity, humility and ownership in high ambiguity

Ethics, Bias & Brand Trust

• Proactively address bias, representation and fairness in AI systems
• Align technical decisions with company values around individuality and body confidence
• Speak up when technical direction risks user trust

Internal AI Adoption (Critical)

• Evaluate and drive adoption of AI productivity tools across Product & Engineering
• Embed AI-assisted development into day-to-day workflows
• Define standards that let us move fast — without building tech debt mountains

Must-Haves

• 3–5 years in a technical leadership role
• Proven track record delivering AI/ML products from inception to production
• Deep hands-on expertise in at least one core ML domain (strong preference for computer vision and/or generative AI)
• Experience with LLMs, conversational AI and evaluation of generative systems
• Strong MLOps and engineering mindset
• Hands-on with AWS, Python, SQL and modern ML tooling
• Strong data engineering and annotation strategy experience
• Experience leading teams and working with senior stakeholders
• Comfortable in fast-moving, evolving environments
• Simplicity mindset: start simple, add complexity only when necessary