Data scientist / data analyst

Job Description:

Role: Data Scientist / Data Analyst — Property Use-Case Modelling

Company: adema.ai (UK PropTech)

Location: Remote-friendly (UK/EU time zones) • Full-time

Mission

Help us add new property use-case analyses (e.g., Residential, Social Housing, Commercial, Care, STR, EV Charging, Data Centres). You’ll research and source datasets, build models that infer demand/supply and revenue potential at the most local level possible, and ship them into our product.

What you’ll do

• Map each “use case” data landscape: Identify, evaluate and acquire structured sources (e.g., prices, rents, demographics, planning, POIs, transport, connectivity) and useful unstructured sources (local plans, market reports, PDFs). Track licence terms and provenance.
• Engineer geospatial & temporal features: Join/clean data, spatially downscale/coalesce (e.g., LA → LSOA/sector/property) using proxies (prices, comps, time-series trends, neighbourhood features, travel times).
• Build predictive/forecast models: Estimate demand, supply, pricing/rent & revenue; quantify uncertainty; design robust validation and back-testing.
• Productionise your work: Persist outputs in Postgres/PostGIS, expose via GraphQL; implement services in Go or Python; write clear SQL views, tests and docs; monitor data quality and model drift.
• Extract signal from unstructured data: Scrape/download reports, parse tables/figures, apply LLM-assisted extraction where useful; convert to structured features.
• Collaborate across the stack:
• With Product to define success metrics and MVP scope per genre.
• With Backend to integrate pipelines/APIs.
• With Frontend/AI teams to shape GraphQL queries and agent/tool schemas.
• Ship iteratively: Prioritise “easier” genres first (Residential, Commercial), then expand to specialised sectors. Document assumptions and limitations.

What you’ve done

• 3+ years in Data Science / Analytics (or 2+ with a strong portfolio) delivering models into production.
• Strong Python (pandas/numpy/scikit-learn; XGBoost/LightGBM; basic PyTorch a plus) and SQL.
• Solid geospatial skills (PostGIS/GeoPandas/QGIS) and time-series/forecasting know-how.
• ETL/ELT and data wrangling at scale; comfort with scraping and PDF/table extraction.
• Good software practice: Git, containers, CI/CD, testing, clear documentation.
• Product mindset: bias to ship, explain results simply, track impact.

Nice to have

• Go, GraphQL, dbt, Airflow/Dagster, FastAPI; Azure.
• UK property/economics exposure (Land Registry, EPC, census/ONS, planning, VOA etc.).
• LLM/AI experience for information extraction or analyst co-pilots.

Success looks like

30 days: Different types of Residential models live in app (Postgres/PostGIS + GraphQL), with documented features, validation and property-level scoring.

Why adema.ai

We’re building the decision layer for UK property — rigorous data, clear modelling, and real-world utility. If you love turning messy datasets into decisive answers, you’ll fit right in.

HOW TO APPLY
Please send your CV to [email protected] and we will come back to you quickly.

Role: Data Scientist / Data Analyst — Property Use-Case Modelling

Company: adema.ai (UK PropTech)

Location: Remote-friendly (UK/EU time zones) • Full-time

Mission

Help us add new property use-case analyses (e.g., Residential, Social Housing, Commercial, Care, STR, EV Charging, Data Centres). You’ll research and source datasets, build models that infer demand/supply and revenue potential at the most local level possible, and ship them into our product.

What you’ll do

• Map each “use case” data landscape: Identify, evaluate and acquire structured sources (e.g., prices, rents, demographics, planning, POIs, transport, connectivity) and useful unstructured sources (local plans, market reports, PDFs). Track licence terms and provenance.
• Engineer geospatial & temporal features: Join/clean data, spatially downscale/coalesce (e.g., LA → LSOA/sector/property) using proxies (prices, comps, time-series trends, neighbourhood features, travel times).
• Build predictive/forecast models: Estimate demand, supply, pricing/rent & revenue; quantify uncertainty; design robust validation and back-testing.
• Productionise your work: Persist outputs in Postgres/PostGIS, expose via GraphQL; implement services in Go or Python; write clear SQL views, tests and docs; monitor data quality and model drift.
• Extract signal from unstructured data: Scrape/download reports, parse tables/figures, apply LLM-assisted extraction where useful; convert to structured features.
• Collaborate across the stack:
• With Product to define success metrics and MVP scope per genre.
• With Backend to integrate pipelines/APIs.
• With Frontend/AI teams to shape GraphQL queries and agent/tool schemas.
• Ship iteratively: Prioritise “easier” genres first (Residential, Commercial), then expand to specialised sectors. Document assumptions and limitations.

What you’ve done

• 3+ years in Data Science / Analytics (or 2+ with a strong portfolio) delivering models into production.
• Strong Python (pandas/numpy/scikit-learn; XGBoost/LightGBM; basic PyTorch a plus) and SQL.
• Solid geospatial skills (PostGIS/GeoPandas/QGIS) and time-series/forecasting know-how.
• ETL/ELT and data wrangling at scale; comfort with scraping and PDF/table extraction.
• Good software practice: Git, containers, CI/CD, testing, clear documentation.
• Product mindset: bias to ship, explain results simply, track impact.

Nice to have

• Go, GraphQL, dbt, Airflow/Dagster, FastAPI; Azure.
• UK property/economics exposure (Land Registry, EPC, census/ONS, planning, VOA etc.).
• LLM/AI experience for information extraction or analyst co-pilots.

Success looks like

30 days: Different types of Residential models live in app (Postgres/PostGIS + GraphQL), with documented features, validation and property-level scoring.

Why adema.ai

We’re building the decision layer for UK property — rigorous data, clear modelling, and real-world utility. If you love turning messy datasets into decisive answers, you’ll fit right in.

HOW TO APPLY
Please send your CV to [email protected] and we will come back to you quickly.