Fresha22h ago

New

GBP 95000–110000/yr

Head of Data Science

LondonFull-timeexecutive

Data ScienceData & AI

0 views0 saves0 applied

Apply Now

Quick Summary

Overview

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

Requirements Summary

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.

Technical Tools

dbtdockersnowflakeab-testingci-cdfintechmachine-learningsaasstakeholder-management

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.

About the Role

~1 min read

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.

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

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

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.

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