Head of Data Science, Fresha

• 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

Desirable

• 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

• 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