How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Head of Data Science
Fresha – Head of Data Science
A 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, barbershops, spas, medspas, fitness studios, and health practices.
Key Stats:
- Trusted by millions of consumers and businesses worldwide
- Powering over 140,000+ businesses and 450,000+ professionals
- Processing over 1 billion appointments to date
Based in London, UK with 15 global offices across North America, EMEA, and APAC.
The Fresha Ecosystem:
Fresha provides a marketplace for consumers to discover, book, and pay for appointments while delivering an all-in-one platform to businesses for:
- Managing appointment bookings
- Handling point-of-sale (POS)
- Maintaining customer records
- Executing marketing automation & loyalty programs
- Tracking beauty product inventory
- Overseeing team management
Our marketplace and advanced tech integrations (with Instagram, Facebook, Google) optimise revenue and engagement for professionals.
With rich behavioural and transactional data, we want data science to unlock greater value.
About The Role
We’re hiring a Head of Data Science to elevate DS from reactive problem-solving to a proactive, foundational function at Fresha.
The Challenge:
- A small but technically skilled team with existing production ML models (fraud detection, text moderation, taxonomy classification).
- Current status: reactive, with unexplored DS opportunities.
The Mandate:
Turn DS into indispensable to Fresha’s decisions, products, and growth.
Suitable only if you’ve done this before: Scale a DS team at a company into a function the business can’t operate without.
Location: Based in Fresha’s London office (5 days/week).
What You’ll Do
Strategy & Influence
- Define a DS roadmap aligned with Fresha’s marketplace, payments, and partner growth priorities.
- Shift culture from reacting to requests → identifying opportunities.
- Build credibility with leadership, making DS a visible and sought-after function.
- Collaborate with Product, Engineering, and Commercial teams for deeper integration.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Delivery & Technical Leadership
- Ship high-impact ML models that drive real business outcomes (not just ivory-tower models).
- Establish experimentation rigor: Implement A/B testing infrastructure, causal inference, and automated optimisation.
- Build DS infrastructure (feature store, model governance, MLOps, CI/CD).
- Stay hands-on but strategic: Evaluate trade-offs, influence technical architecture.
- Engage in high-impact projects like Merchant Liability Optimization & Growth Platforms.
Visibility & Advocacy
- Champion DS internally: Demonstrations, stakeholder education, proactive engagement with Product Managers.
- Build external visibility: Engineering blogs, conference speaking, industry thought leadership.
- Attract top DS talent by making the function known.
Team Building
- Scale the team in line with roadmap needs (ML engineers, DS specialists, MLOps).
- Develop and mentor existing team; create career paths; define technical and cultural standards.
What The First Year Looks Like
✅ 3 Months:
- DS roadmap define, sign-off across functions.
- New high-value use cases identified.
- First proofs-of-concept proposed.
✅ 6 Months:
- Multiple ML/AI use cases live or in evaluation.
- Experimentation discipline active in one product line.
- DS gains internal visibility (demos, showcase work, external presence).
✅ 12 Months:
- DS established as critical, embedded function, proven over time.
- Fewer DS-powered projects delayed because they don’t integrate with other teams.
- Notable growth in team (scaling to achieve impact).
- MLOps maturity of team enhances efficiency.
What You Bring
Must-Have
- 4–5 years+ experience in data science, ML engineering, data ops/prod software.
- 3+ years directly managing and growing DS teams.
- Track record of building DS from small to fundamental—not just inheriting one.
- Shipped models at scale with tangible business impact.
- Strong stakeholder management (comfortable influencing C-suite and leaders).
- Technical depth: Evaluates designs, recognises trade-offs.
- Experience developing people: mentored engineers → DS leads, created career frameworks.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Nice-to-Have
- Industry fit: Experience in marketplace, SaaS, or fintech—fixing technical debt, growing teams.
- Familiarity with Fresha’s stack: SageMaker, Snowfall (was Snowflake), dbt, Docker.
- Repeat design wins: Built/organised feature stores, MLOps, or A/B testing platforms.
- աբեր الحوافز: External thought leadership (blogs, talks, contributions).
- Proven ownership: Made data science a “core function” for previously absent roles.
Why This Role?
Data Scale & Opportunity
Millions of transactions, 120+ representative countries, and behaviour insights: The ceiling is high.
Technical Foundation & (Near) Zero To-Go
- Running models in production now.
- Large datasets already collected and modelled.
- Your job is accelerating their potential.
Tangible Business Impact
DS initiatives move needle on key metrics directly.
Interview Process
- Screen Stage: 30-min video call (Talent Team).
- 1sts Stage: Google Hangout (60 mins)—soft/hard skill variety.
- 2nd Stage: In-person (live case study & review). Don’t role-play (it’s prospective).
- Final Stage: Stakeholder interview with Deputy CPO OR CTO (60 mins).
We’ll complete the entire process and provide feedback in 4 weeks.
All applications are individually reviewed by the Fresha team. Typically, within 7 days—though we do receive high-volume attention, so occasional delays naturally occur.
Inclusive Workforce
At Fresha, we promote an inclusive culture where everyone’s ideas, perspectives, and voices matter.
- Fair and equitable selection—no discrimination based on:
- Race
- Religion
- Sex, sexual orientation, age
- Gender identity
- Disability
- or other lawyer-protected characteristics unique to your jurisdiction.
- Accessibility matter? Get in touch—we’ll do everything reasonably legal to ensure comfort during the interview process .
Be here!
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location