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Dwelly is building an AI operating system for residential lettings, operating over 15,000 properties and $470M in GMV across the UK. You will be the first dedicated data scientist in the newly launched Growth function, owning the analytical agenda alongside commercial and product leads. This is a thought-partner role where you bring hypotheses, challenge priorities, and build analyses that drive decisions—not a service seat.
You will work with an exceptional data asset: years of conversations with landlords and tenants, complete property management job records, payment and arrears history, and thousands of call recordings. With LLMs, this unstructured data becomes a competitive feature store unavailable to competitors.
Key responsibilities include: (1) Churn early-warning that identifies root causes (e.g., service failures vs. property sales) and quantifies impact for targeted retention; (2) Share-of-wallet expansion by estimating hidden landlord portfolios and ranking by expected units won; (3) Pricing and elasticity analysis across acquired agencies with segment-level recommendations; (4) Rent Guarantee underwriting using proprietary loss data; (5) Growth experimentation design with proper statistical rigor; (6) Building the Growth data layer including pipelines from platform databases, payments, comms, call transcripts, and PM jobs.
You will own full-stack analytics: Python, SQL, dbt-style pipelines, and data engineering. Statistical depth is essential—experiment design, causal inference (diff-in-diff, matching, synthetic control), survival analysis, elasticity estimation. You apply pragmatic ML (churn propensity, uplift modeling, pricing, forecasting) and are fluent with LLMs on messy text and audio, building eval sets to validate trustworthiness. You prefer well-calibrated production models over experimental notebooks.
The role offers the chance to define metrics and analytical priorities from scratch rather than inherit legacy dashboards, and to observe how companies scale from 0→1→N. Dwelly is backed by top-tier investors and led by founders with deep real estate and technology experience (prior success with PIK Arenda).
