• Develop and maintain innovative models for asset valuation. This requires thorough statistical analysis of the underlying data and understanding of the various macroeconomic factors and risk factors that impact prices and liquidity in the real estate market. • Develop models for portfolio management purposes such as value forecasting or market risk segmentation. • Closely provide regular ongoing model performance assessments and enhance existing models. • Develop analytical tools to support other teams • Working with the technology team, drive initiatives to enhance data capabilities and streamline infrastructure. • Work closely with engineers/data infrastructure team in designing useful data attributes. • Perform exploratory analysis and make recommendations on improving current data models, decisioning frameworks, etc. • Refactor existing analytic solutions to improve scalability.

• Model building, domain research, data visualisation and reporting • Working closely with software developers and consultants to grow and develop new avenues of business • Influencing technical direction and architectural decision-making • Delivering significant new features and applications, developing industry-leading analytics and reporting capabilities

• Apply the right analytical methods-statistical analysis, regression, clustering, forecasting, classification-to solve real business problems at scale • Design and build end-to-end analytical solutions-from data extraction in BigQuery and MariaDB to actionable insights and automated reporting • Develop and maintain data pipelines, analytical frameworks, and tooling using Python and SQL • Use AI-powered development tools (Windsurf, Gemini, Claude, Cursor) to accelerate experimentation, code generation, and analysis • Leverage the latest AI features and tools (LLMs, embeddings, generative AI) to augment and automate analytical workflows • Build lightweight applications and APIs (FastAPI) to put models and insights into the hands of stakeholders • Own the data architecture decisions within your domain-schema design, data modeling, and pipeline reliability • Communicate results, trade-offs, and recommendations clearly to both technical and business audiences • Map and improve business processes through data-driven analysis and process design

• Apply the right analytical methods-statistical analysis, regression, clustering, forecasting, classification-to solve real business problems at scale • Design and build end-to-end analytical solutions-from data extraction in BigQuery and MariaDB to actionable insights and automated reporting • Develop and maintain data pipelines, analytical frameworks, and tooling using Python and SQL • Use AI-powered development tools (Windsurf, Gemini, Claude, Cursor) to accelerate experimentation, code generation, and analysis • Leverage the latest AI features and tools (LLMs, embeddings, generative AI) to augment and automate analytical workflows • Build lightweight applications and APIs (FastAPI) to put models and insights into the hands of stakeholders • Own the data architecture decisions within your domain-schema design, data modeling, and pipeline reliability • Communicate results, trade-offs, and recommendations clearly to both technical and business audiences • Map and improve business processes through data-driven analysis and process design