Sr. Data Scientist / Machine Learning Engineer

About Smart Bricks

Smart Bricks is an AI-powered real estate investment platform transforming how individuals and institutions access global property markets. With strong traction across the UAE, UK, and US, our platform uses predictive modeling, Automated Valuation Models (AVMs), and Agentic automation to streamline real estate investing — from discovery to transaction. Data is the core of our competitive advantage.

About the Role

• Maintain reproducible research, document assumptions, and communicate model behavior to stakeholders.
• Build and own AVMs and predictive models from data ingestion to deployment
• Analyze, clean, and structure large property datasets (listings, transactions, geospatial data, etc.)
• Develop feature engineering pipelines to extract insights from structured and unstructured data
• Use machine learning and statistical modeling to estimate property values and market trends
• Collaborate with data engineers and product teams to integrate models into production systems
• Continuously monitor and improve model accuracy, performance, and scalability
• Communicate insights and model behavior clearly to technical and non-technical teams
• Role is Remote or Hybrid.

Required:

• 5+ years of hands-on experience in machine learning, data science, and AI product development
• Strong command of Python and key libraries: pandas, numpy, scikit-learn, xgboost, lightgbm, statsmodels
• Experience with end-to-end model lifecycle — data preprocessing → training → validation → deployment
• Proven experience building regression or valuation models (property, finance, or asset pricing a plus)
• Strong understanding of statistical modeling, feature engineering, and model evaluation
• Solid experience with Sql, Big Query and cloud data environments (GCP )
• Working familiarity with geospatial data (e.g., shapefiles, GeoPandas, GIS concepts)

Preferred / Nice to Have:

• Background in proptech, fintech, or related sectors
• Exposure to deep learning (TensorFlow / PyTorch) or image-based valuation models
• Familiarity with API design, microservices, or MLOps tools (e.g., MLflow, Kubeflow)
• Knowledge of real estate data ecosystems — transaction, listing, or price index data
• MS or PhD in Data Science, Computer Science, Statistics, or a quantitative discipline