Head of Data Science, Fresha

• Track record of building a data science function from small into something the business relies on - not inheriting one
• Shipped ML models to production at scale with measurable business outcomes
• Exec-level stakeholder communication - comfortable making the case for data science investment to C-suite, product, and commercial leaders
• Technical depth to evaluate architecture, review work, and call the right trade-offs
• Hands-on comfort - reviewing code, contributing to architecture, getting into the weeds when needed
• 4+ years in data science or ML engineering, with 3+ years directly managing data science teams

Desirable

• We're hiring a Head of Data Science to build data science into a core function at Fresha, not manage what already exists.
• The team is small but technically strong - production ML in fraud detection, text moderation, and taxonomy classification, running on SageMaker with a dbt/Snowflake stack.
• We're operating reactively, and there's significantly more value data science can unlock across the marketplace
• You'll have leadership buy-in and a technically strong team already in place.
• Your job is to set the direction, grow the team, and shift data science from a service function to something the business builds around.
• This role is right for you if you've done this before - taken a small data science team at a scaling company and turned it into something the business can't operate without
• Define the data science roadmap aligned to Fresha's business priorities across marketplace, payments, and partner growth
• Identify data science opportunities that move revenue or cost - and deprioritise the rest
• Make the case for data science investment at the exec table
• Ship ML products that drive measurable business outcomes - not just models
• Establish experimentation as a discipline: A/B testing, causal inference, automated experimentation
• Build foundational data science infrastructure: feature store, model governance, monitoring, CI/CD for ML
• Stay hands-on enough to evaluate architecture, hold technical trade-offs, and contribute to high-impact projects
• Champion data science internally through demos, stakeholder education, and proactive engagement with product and commercial teams
• Scale the team as the roadmap demands - ML engineering, data science, MLOps
• Develop the existing team, create career paths, set technical and cultural standards
• 3 months: Data science roadmap defined cross-functionally and signed off. High-impact use cases on the table the business hadn't previously identified. First POCs or MVPs in flight. Data science visibly present in product planning - already shifting from reactive to proactive
• 6 months: Multiple ML/AI use cases shipped or in live evaluation. Experimentation active in at least one product area. Data science achievements visible internally through demos and showcases; early external presence building
• 12 months: Data science is a recognised, embedded function with a track record of delivery. Experimentation is a working discipline used beyond data science. MLOps maturity has stepped up. The team has grown in line with what was needed to get here