Head of Data Science

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.

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

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

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

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.

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.

Screen Stage - Video-call with a member from the Talent Team (30mins)

1st Stage - Google Hangout - soft skills & technical skills (60 mins)

2nd Stage - In-person case study + live review with Team (60 minutes)

Final Stage - Stakeholder interview with Deputy Chief Product Officer OR Chief Technology Officer (60min)