About Fresha
Fresha is the AI‑powered operating system for the global beauty, wellness and self‑care industry, connecting and powering everything from salons and barbers to spas, medspas, fitness studios and health practices.
Trusted by millions of consumers and businesses worldwide. Fresha is used by 140,000+ businesses and 450,000+ stylists and professionals worldwide, processing over 1 billion appointments to date.
The company is headquartered in London, United Kingdom, with 15 global offices located across North America, EMEA and APAC.
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.
Overall, 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.
Location: London – the Bower, 207-122 Old Street, London EC1V 9NR (dog‑friendly office, 5 days per week).
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 make 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.
Benefits
- 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.
Interview Process
- Screen Stage – Video‑call with a member from the Talent Team (30 min).
- 1st Stage – Google Hangout – soft skills & technical skills (60 min).
- 2nd Stage – In‑person case study + live review with Team (60 min).
- Final Stage – Stakeholder interview with Deputy Chief Product Officer OR Chief Technology Officer (60 min).
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.
£95,000 – £110,000 a year
Inclusive Workforce
At Fresha, we are creating a culture where individuals of all backgrounds feel comfortable.
We want all Fresha people to feel included and truly empowered to contribute fully to our vision and goals. Everyone who applies will receive fair consideration for employment.
We do not discriminate based on race, colour, religion, sex, sexual orientation, age, marital status, gender identity, national origin, disability, or any other applicable legally protected characteristics in the location in which the candidate is applying.
If you have any accessibility requirements that would make you more comfortable during the interview process and/or once you join, please let us know so that we can support you.
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