Head of Data Science

We process millions of transactions and generate rich behavioural data across consumers and partners. Despite this, data science is still early at Fresha. That's the opportunity.

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.

What You'll Do

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

What the First Year Looks Like

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.

What You Bring

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

Interview Process

• 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)

We aim to finalise the entire interview process and deliver feedback within 4 weeks.

Every job application received is reviewed manually by our talent team. While we strive to assess applications within 7 days, the sheer volume of talented individuals expressing interest may occasionally extend this timeframe