Head of Data Science (Hiring Immediately)

About Hey Savi

Hey Savi is a fashion tech startup here to spark a new era in fashion discovery, igniting body confidence in everybody and every body. Think Shazam meets Spotify, but for fashion. As a female-founded startup, we’re at an exciting stage and shaping something truly game-changing in the world of fashion discovery.

Our community doesn’t want to scroll through paid ads and dead-end links. They want fashion that fits – in stock, in their size and at the best price – with a search experience that puts them at the centre. We’re creating a movement, not just a product, and we're looking for someone brilliant to help lead the charge.

The Role: Head of Data Science

We’re looking for a hands-on Head of Data Science to own and build the intelligence at the heart of Hey Savi.

This is a true player–coach role. You will personally design, build, deploy and iterate on production AI systems while shaping the long-term data and AI strategy of the company.

You will work extremely closely with the founders, product and engineering teams to decide what to build, what not to build and when “good enough” is the right answer.

If you’re keen for high agency, genuine ownership, big impact, and the chance to build alongside a talent-dense team in a flexible, collaborative setup this could be your perfect role. However, if you’re looking to manage a large team, optimise an already-mature platform, or focus purely on research - this role is not for you.

Requirements

What You'll Be Responsible For

Hands-on AI & Data Leadership

• Personally design, build and deploy production computer vision and agentic AI systems that power search, discovery, recommendations and personalisation.
• Own the full data science lifecycle: problem framing, data exploration, modelling, evaluation, deployment, monitoring and iteration.
• Make pragmatic trade-offs between speed, quality and technical elegance in an early-stage environment.

Product & User Impact

• Translate messy user problems into clear, testable interventions.
• Partner with Product to ensure models improve user trust, confidence and discovery - not just offline metrics.
• Laser focus on feature value, return on investment, and optimising for the right signals

Data Reality & Foundations

• Work hands-on with imperfect datasets.
• Design annotation strategies, data 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: deployment, monitoring, alerting and iteration.
• Work closely with Engineering to build scalable but lightweight data and ML pipelines (AWS).
• Ensure models are robust, reliable, scalable, explainable where needed and safe to operate in production.

Team & Culture (Now and Future)

• Set a strong technical and ethical bar for data science at Hey Savi.
• Mentor and guide future data scientists as the team grows.
• Model curiosity, humility and ownership in a high-ambiguity startup environment.

Ethics, Bias & Brand Trust

• Proactively consider bias, representation and ethical implications of AI in fashion.
• Ensure our systems align with Hey Savi’s values around body confidence, individuality and trust.
• Speak up when a technical direction risks harming user trust or brand integrity.

Internal AI Adoption (Critical)

• Evaluate, select and drive adoption of AI productivity tools across Product, Engineering, and Data Science
• Embed AI-assisted development into day-to-day technical workflows
• Define usage standards and ways of working (we want to move fast without accruing mountains of tech debt!)

Must-haves

• 3-5 years in a technical leadership role
• Proven success delivering AI/ML products from inception through production in a commercial setting.
• Deep hands-on expertise in at least one core ML domain (strong preference for computer vision and/or generative AI), with solid theoretical knowledge across multiple areas.
• Experience with LLMs, conversational AI and evaluation of generative models.
• Strong MLOps and engineering mindset, including CI/CD, automated deployment, and model monitoring.
• Hands-on proficiency with AWS, Python, SQL, and modern ML tooling.
• Strong understanding of data engineering, data quality, and annotation strategies.
• Experience building and leading teams and working effectively with senior stakeholders.
• Comforta]]>