Job Description
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