You will be: designing and developing statistical models for property price adjustments across time, location, quality, and condition, building spatial algorithms (adaptive heatmaps, geographic clustering, polygon-based property search) to capture local market dynamics, implementing comparable property recommendation with feature engineering across different property types, developing market analysis pipelines with solid diagnostics: trend fitting, outlier detection, goodness‑of‑fit metrics, integrating LLM‑based classification services for document and property analysis, exposing model outputs through production API endpoints and working with frontend engineers on data contracts, debugging models in production: edge cases, numerical issues, data quality problems. Your profile: solid statistics background: regression, GAMs, mixed/random effects, link functions, robust estimation, outlier handling, proficiency in Python and the data science stack: NumPy, Pandas, statsmodels, SciPy, scikit‑learn, experience building and maintaining production APIs with FastAPI and Pydantic, comfortable working with PostgreSQL and SQLAlchemy, familiar with containerized environments (Docker, Kubernetes, GCP), able to turn domain requirements into quantitative solutions and communicate trade‑offs, good command of English (spoken and written), familiarity with basic statistical concepts (e.g., Bayes’ rule, linear regression, maximum likelihood estimation). Nice to have: geospatial data and libraries (GeoPandas, Shapely, H3, GeoAlchemy2), GAM libraries (PyGAM), JAX, or TensorFlow Probability, task queues and async workflows (Celery, Redis), observability tooling (OpenTelemetry), data validation and property‑based testing (Pandera, Hypothesis, TestContainers), R integration (rpy2), LLM integrations (Google Gemini or similar), frontend awareness (React, TypeScript), real estate data, valuation methodology, or appraisal workflows. Work from the European Union region and a work permit are required. #J-18808-Ljbffr
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