Founding ML/Data Scientist

About Us

We're an AI-driven hospitality tech platform helping vacation rental owners automate their revenue on Booking.com and Airbnb.

We sit on Booking.com private API data, Airbnb channel data, and scraped competitive pricing across thousands of properties — and right now we barely use it. Our agent is rules-based where it should be data-driven, and our promotions engine has no model behind it. We're looking for a founding ML/Data Scientist to change that. You'll turn dormant data into a smarter agent and a compounding moat.

The Role

You'll be the first person to seriously dig into our data, find the signals that drive revenue, and work with engineering to make the agent act on them.

• Find what matters: Dig through Booking.com, Airbnb, and competitive data to separate signal from noise.
• Separate cause from correlation: When a promotion drives a booking, was it causal? When occupancy drops, is it pricing, seasonality, or competition? We want rigorous answers, not guesses.
• Turn insights into revenue: Translate findings into logic the agent can use, measurably better than a human's.
• Shape the data strategy: Decide what to track, what to enrich, and where the highest-value opportunities are.
• Ship to production: This isn't a notebooks role. You'll work inside our Django + ClickHouse stack alongside the engineering team.

Logistics

Remote-first for UK-based candidates. Expect to be in London for WeWork sessions a few days a month, flexibly, for onboarding, planning, and product work.

Interview Process

1. 30-min non-technical call with the CTO
2. Take-home data challenge using a sample of real OTA data
3. 1-hour deep dive on your submission with the founding team
4. In-person meeting with both founders
5. Offer

Why This Role

• Unique data: Booking.com private API data, Airbnb channel data, and scraped competitive pricing. This dataset doesn't exist at most companies.
• Real impact: Your models drive revenue decisions for thousands of properties.
• High equity for our stage.
• A compounding moat: Every property we add makes the data richer and the models better.

Salary & Equity

£70k–£100k · Equity 0.5%–1%

Who You Are

• A real data scientist who reaches for statistics and experiments first, and knows when a result is significant versus noise. Strong with time series.
• Causal by instinct — you don't confuse correlation with causation. You can design holdouts and A/B tests, and reason about confounders when a clean experiment isn't possible.
• Comfortable in the mess — real OTA data is dirty, sparse, and seasonal. You wrangle it, find the signal, and build features that predict revenue.
• Fluent in modelling — forecasting, uplift and causal models, and the judgement to pick the simplest thing that works.
• Ships to production — not a notebooks role. You write clean, deployable Python (pandas, NumPy, scikit-learn) and get your models live in our Django and ClickHouse stack.
• A product thinker who cares about revenue impact, not what's technically interesting.
• AI-first — you use AI coding tools with multiple agents to ship faster, ideally pushing into self-learning ML or agentic workflows. If you don't already use frontier AI tools, this isn't the role for you.

Skills

Python · pandas · NumPy · scikit-learn · Time Series · Causal Inference · Forecasting · Experimentation · PostgreSQL · ClickHouse · Django · AI agents

Location

Remote - London UK, United Kingdom