Head of Data Science

The AI-powered OS for beauty, wellness and self-care

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.

Fresha allows consumers to discover, book and pay for beauty and wellness appointments with local businesses via its marketplace, while beauty and wellness businesses and professionals use an all-in-one platform to manage their entire operations with an intuitive business software and financial technology solutions.

Fresha’s ecosystem gives merchants everything they need to run their business seamlessly by facilitating appointment bookings, point-of-sale, customer records management, marketing automation, loyalty, beauty products inventory and team management.

The consumer marketplace unlocks revenue potential for partner businesses by leveraging the power of online bookings and automated marketing through mobile apps and advanced integrations with major tech brands including Instagram, Facebook and Google.

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.

The Mandate

Solidify data science as a core function at Fresha. Set the data science agenda - don't wait to be handed one. Identify the work that moves revenue or cost, prioritise ruthlessly, and get exec buy-in to make it happen. Champion data science across the business until the function is indispensable to how Fresha makes decisions and builds products.

About the Role

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.

To foster a collaborative environment that thrives on face-to-face interactions and teamwork, this role will be based in our dog-friendly office 4 days per week in London: The Bower, 207-122, Old Street, London EC1V 9NR.

What You'll Do

Set the agenda & prioritise ruthlessly

• 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 impact & build the technical foundation

• 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

Build the function

• 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

What the First Year Looks Like
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.

What You Bring

Must-Haves

• 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

Nice-to-Haves

• Marketplace, SaaS, or fintech experience

• Familiarity with our stack (SageMaker, Snowflake, dbt, Docker)

• Built or contributed to experimentation platforms or MLOps infrastructure

Why This Role

Real data, real scale. Millions of transactions monthly across 120+ countries, rich behavioural signals across a two-sided marketplace. The data is there - the value is largely un-unlocked.

Strong technical foundation. Production ML stack, a team with deep context across the data and business, working models live. You're accelerating, not bootstrapping.

Visible impact. At Fresha's stage, data science improvements flow directly to business metrics. This isn't optimising the fifth decimal place - it's building capabilities that don't exist yet.

Exec-level ownership. You'll be at the table with the CTO and Deputy CPO, shaping the data science function from the ground up.

Interview Process

• Screen Stage - 30 min call with the Talent Team

• 1st Stage - 60 min video interview: soft skills & technical

• 2nd Stage (Tactical) - 60 min get-to-know session with the data science team

• 3rd Stage (Strategic & Technical Case Study) - 2 hours onsite: case study with the data science + engineering team

• Final Stage - 60 min strategic conversation with the CTO and Deputy Chief Product Officer

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.

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.