Data Scientist (Growth Analytics)

Position Summary

We are launching Growth as a dedicated direction — everything that moves units on the platform and revenue per unit. You will be the first data person in it, owning the analytical agenda together with the commercial and product leads rather than servicing a queue of chart requests. A thought-partner role: bring hypotheses, argue about priorities, say when a plan won’t work, then build the thing that settles it.

A note on the title. We have always asked our analysts for statistics, real programming, data pipelines and modest ML — we just never wrote it down. We are now naming the job the way the market names it. If "Data Scientist" means a research seat with a clean feature store handed to you, this isn’t that. If it means going from raw messy data to a decision without waiting for anyone, it is exactly that.

A normal estate agency knows almost nothing about its own business: a CRM with names, a bank feed, and the memory of whoever has worked there longest. We are in a different position. Across a portfolio of agencies we hold years of conversations with landlords and tenants, every property management job with the full record of what went wrong, every payment and arrear, and thousands of hours of calls. Most of it is unstructured, which until recently meant unusable. With LLMs it is a feature store — and that changes the class of question we can answer. No agency on the island can do this, and few proptech companies can.

Why it's cool: a data asset nobody else in this market can replicate — years of conversations, jobs, payments and calls, and permission to point LLMs at all of it; experienced founders who have already walked this path before (built PIK Arenda), and now significant traction shown in the UK already; a large but compact and well-capitalized market of 20 thousand agencies on the island; Growth starts now, so you define the agenda and the metrics instead of inheriting legacy dashboards; and we are now on the way from 0 to 1, then there will be scaling 1 → N, so there's an opportunity to see how companies of different stages grow and develop.

Key Responsibilities

1. Churn Early-Warning

• Churn early-warning that names the cause, not just the risk. Combine arrears, job SLA breaches and tenancy events with intent and sentiment extracted from conversations and call recordings. Separate the landlord who is selling the flat from the one we lost through a botched boiler repair — different playbooks, one goes to retention, the other straight into the sales funnel — and put a pound figure on each cause so operations can prioritise honestly.

2. Share-of-Wallet Expansion

• Turn share of wallet from a survey anecdote into a ranked call list. Landlords hold roughly 60 properties off-platform for every 100 they place with us. Estimate each landlord’s hidden portfolio, rank by expected units won, then mine the resulting call recordings for why they said no — that is usually where the next product comes from.

3. Pricing & Elasticity

• Find the price sensitivity of the landlord base. Our acquired agencies charge wildly different fees. Reconstruct what is actually charged, estimate elasticity by segment, recommend the maximum defensible uplift — then hold yourself to your own churn forecast and correct the model. The same machinery prioritises the rent review backlog by expected pounds.

4. Rent Guarantee Underwriting

• Underwrite Rent Guarantee off our own loss book. Probability of default and severity from our arrears and collections history, real pricing, eligibility rules, and monitoring that flags a deteriorating book early. We own the loss data, which is why we can build this and a broker can’t.

5. Growth Experimentation

• Make growth experiments actually readable. Tenant-side products, opt-in payment flows, upsell paths and outreach sequences — designed with holdouts, power and an uplift estimate, not a before-and-after chart.

6. Growth Data Layer

• Own the Growth data layer. Pipelines from the platform database, payments, comms, call transcripts and PM jobs; the metrics tree everyone argues from; and an eval harness for the LLM extraction, because a classifier nobody has measured is a rumour.

Qualifications and Preferred Background

• Strong communication skills and fluency in English.
• Higher degree education.
• Startup mentality: resilience, adaptability, and ability to thrive in a fast-paced environment.
• Customer-centric mindset: focus on delivering value to end-users or clients.
• Strong problem-solving skills – ability to approach challenges logically and propose practical solutions.
• 5+ years of experience in the role of DA / DS.
• Possess the high level of autonomy and "full-stack analyst" skill set expected of a Senior DA grade.
• Write Python and SQL and build your own pipelines (dbt or equivalent). Data engineering is part of the job, not an adjacent one.
• Have real statistical depth: experiment design, causal inference without randomisation (diff-in-diff, matching, synthetic control), survival analysis, elasticity. You volunteer the caveat before someone else finds it.
• Apply pragmatic ML — churn propensity, uplift, pricing, simple forecasting. For the current state we prefer a well-calibrated logistic regression in production to a gradient boosting model in a notebook.
• Are fluent with LLMs on messy text and audio, and know how to build eval sets to prove the output is trustworthy.
• Are comfortable reading backend code and fetching the data yourself when nobody has prepared it.
• Work as a thought partner to business and product — you form your own view and push back. Not a ticket-taker.
• Nice to have: have priced or underwritten a financial product — insurance, guarantee, credit — off your own loss data; enjoy being the first analyst in a direction and defining the metrics tree from scratch; and like talking to Managing Directors and agents and learning the domain first-hand, rather than only through the database.