ExecutiveFull TimeEN
About the Role
We're hiring a Head of Data Science to build DS into a core function at Fresha, not manage what already exists. Today the team is small but technically strong. We have production ML models in fraud detection, text moderation, and taxonomy classification, running on SageMaker with a dbt/Snowflake data stack. But we're operating reactively, and we know there's significantly more value DS can unlock across the marketplace.
You'll have a clear mandate, leadership buy-in, and a technically strong team already in place. Your job is to set the direction, grow the team, and make data science visible and indispensable to how Fresha makes decisions and builds products.
This role is right for you if you've done this before - taken a small DS team at a scaling company and turned it into something the business can't operate without.
To foster a collaborative environment that thrives on face-to-face interactions and teamwork, this role will be based in our dog-friendly office 5 days per week in London: The Bower, 207-122, Old Street, London EC1V 9NR.Strategy & Influence
Define the DS roadmap and align it to Fresha's business priorities across marketplace, payments, and partner growth
Shift DS from reactive (responding to product requests) to proactive (identifying opportunities, building POCs, running demos)
Build DS credibility with leadership - make the function visible, understood, and sought out
Partner with Product, Engineering, and Commercial teams to embed DS into decisions
Delivery & Technical Leadership
Ship ML products that drive measurable business impact - not just models, but outcomes
Establish experimentation as a discipline: A/B testing infrastructure, causal inference, automated experimentation for optimisations
Build foundational DS infrastructure: feature store, model governance, monitoring, CI/CD for ML
Stay hands-on enough to evaluate technical decisions and architecture trade-offs
Contribute directly to high-impact projects when needed
Visibility & Advocacy
Champion DS internally through demos, stakeholder education, and proactive engagement with PMs
Drive external visibility: engineering blog posts, conference talks, thought leadership
Help Fresha attract top DS talent by making the function known
Team Building
Scale the team in line with what the roadmap demands - hiring across ML engineering, data science, and MLOps
Develop the existing team, create career paths, and set technical and cultural standards
3 months: DS roadmap defined cross-functionally and signed off. New high-impact use cases on the table that the business hadn't previously identified. First POCs or MVPs in flight. DS is visibly present in product planning — already shifting from reactive to proactive.
6 months: Multiple ML/AI use cases shipped or in live evaluation. Experimentation is active in at least one product area. DS achievements are visible internally - demos, showcases, early external presence.
12 months: DS is a recognised, embedded function with a track record of delivery. Experimentation is a working discipline used beyond DS. MLOps maturity has stepped up. The team has grown in line with what was needed to get here.
Must-Haves
4-5 years in data science, ML engineering, or related technical fields
3+ years directly managing and growing DS teams
Track record of building a DS function - not just inheriting one. You've taken a team from small to meaningful and made DS matter to the business
Shipped ML models to production at scale with real business outcomes
Strong stakeholder management - comfortable influencing C-suite, product leaders, and commercial teams
Technical depth to evaluate architecture decisions, review work, and call the right trade-offs
Experience developing people - grown ICs into leads, created career ladders, built team culture
Nice-to-Haves
Experience in the marketplace, SaaS, or fintech businesses
Familiarity with our stack: SageMaker, Snowflake, dbt, Docker
Built or contributed to feature store, MLOps, or experimentation platform infrastructure
Experience in establishing experimentation and A/B testing as an organisational practice
Thought leadership - blog posts, talks, open-source contributions
Experience making DS a "core function" at a company where it previously wasn't
Real data, real scale. Millions of transactions, 120+ countries, rich behavioural signals across a two-sided marketplace. The data is there, and there's significantly more value to unlock.
Strong technical foundation. You're not starting from zero. There's a production ML stack, a team with deep context across the data and business, and working models in production. You're accelerating, not bootstrapping.
Visible impact. At Fresha's stage, DS improvements flow directly to business metrics. This isn't optimising the fifth decimal place - it's building capabilities that don't exist yet.
