Note: The job is a remote job and is open to candidates in USA. Dwelly is building an AI operating system for residential lettings. The Senior Data Scientist will be the first data specialist dedicated to Growth, owning the analytical agenda, developing growth models and experiments, and building data pipelines and evaluation systems across operational, financial, communications, and product data.
Responsibilities
- 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
- 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
- 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
- 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
- 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
- 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
Skills
- 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
- 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
- For the current state we prefer a well-calibrated logistic regression in production to a gradient boosting model in a notebook
- 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
Benefits
- Potential for equity options based on performance, recognising exceptional contributions to our integration success.
Company Overview