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Dwelly is building an AI operating system for residential lettings across the UK and Europe. The company operates over 15,000 properties with $470M in GMV and has raised $263M from top-tier investors. You will be the first dedicated data scientist in the newly launched Growth function, owning the analytical agenda alongside commercial and product leadership.
This is a thought-partner role where you bring hypotheses, challenge priorities, and build analyses that drive decisions—not a service seat. You'll work with an exceptional data asset: years of structured and unstructured data including conversations with landlords and tenants, property management records, payments, arrears, and call recordings. With LLMs now making unstructured data usable, you can answer questions no other proptech company in the market can.
Key responsibilities include: building churn early-warning systems that identify root causes (e.g., service failures vs. property sales) and quantify impact; estimating landlord share-of-wallet expansion by analyzing hidden portfolios and mining call recordings for objection reasons; analyzing price sensitivity and elasticity across acquired agencies to optimize pricing and rent review prioritization; underwriting Rent Guarantee products using proprietary loss data; designing growth experiments with proper holdout groups and uplift estimation; and owning the Growth data layer including pipelines from platform databases, payments, comms, transcripts, and PM jobs.
You'll need 5+ years as a data analyst or data scientist with full-stack capabilities: Python, SQL, and pipeline building (dbt or equivalent). Statistical depth is essential—experiment design, causal inference (diff-in-diff, matching, synthetic control), survival analysis, elasticity estimation. You should apply pragmatic ML (churn propensity, uplift modeling, pricing, forecasting) and be fluent with LLMs on messy text and audio data, including building eval sets to validate output quality. You'll define metrics, own data quality, and operate with high autonomy in a fast-paced startup environment.
