Data Scientist (3-5 years)

Overview

Data Scientist working on a real estate ontology: the entities, relationships, rules and semantics that underpin MRI's Common Data Model, turning them into forecasting, analytics, and ontology-grounded AI products.

What you'll do

• Help turn the Common Data Model into a real estate ontology covering entities, relationships, constraints and business rules across leasing, property operations, assets, investments, and facilities.
• Explore the ontology with data to find high-value solutions such as forecasting, anomaly detection, entity resolution, decision modeling / recommendation, valuation signals, and portfolio optimization.
• Build and validate models on real product data from across the MRI portfolio, with clear baselines and honest evaluation.
• Prototype ontology-grounded AI: knowledge-graph retrieval, LLM agents that reason over typed entities, and natural-language analytics with a semantic layer.
• Define metrics and a semantic layer so that a number means the same thing in every product.
• Partner with product, engineering and domain experts to move proven prototypes into the MRI platform.
• Raise the bar on data quality by finding mapping gaps, bad joins and silent failures, and feeding them back into the model.

What you'll need

• Bachelor's, master's or PhD (from Premier Institute) in Statistics, Mathematics, Computer Science, Data Science, Physics or a related quantitative field.
• 3-5 years of applied data science experience.
• Strong foundations in statistics, probability, optimization and machine learning, and the judgment to know when a simple model is enough.
• Fluent Python and SQL.
• Comfort with modern data stacks such as Spark, a cloud warehouse such as Snowflake or Databricks, dbt or similar.
• Hands-on experience with at least one of: knowledge graphs, ontologies or semantic modelling, entity resolution, or LLM-based applications (retrieval, agents, evaluation).
• Strong verbal and written communication skills.

Nice to have

• Experience in real estate, proptech, finance, or any other asset-heavy domains.
• Published research, patents, competition results, or open-source work.
• Experience taking a model from notebook to production with MLOps practices.

Details

• Location: Delhi/NCR.