Job Description

Data Scientist (AI & Data Engineering) – External Data & Intelligent Insights

Location

Toronto, ON (Hybrid)

Role Summary

Our client is a global real estate investor, developer, and manager connects people to exceptional places across the office, logistics, residential, and retail sectors, operating an elite international portfolio with a focus on long-term value creation.

We are seeking a high-performing Data Scientist with strong data engineering capabilities to build advanced analytical models and intelligent insights. In this role, you will combine external market data (e.g., CoStar, JLL, CBRE) with internal enterprise datasets within our modern Microsoft Fabric data platform.

You will operate across the full data lifecycle: data ingestion, transformation, predictive modeling, and insight generation. Your primary focus will be integrating multi-source external data, engineering governed and reusable datasets, and developing predictive frameworks that unlock high-value business intelligence across enterprise use cases.

Key Responsibilities

1. Data Integration & Engineering (Hands-on)

• Ingest external datasets Source and integrate vendor data including market, macro, demographic, and rental datasets.
• Blend enterprise data Combine external data with internal systems like Yardi, asset management systems, and investment databases.
• Build Fabric pipelines Develop robust pipelines using Microsoft Fabric, including Dataflows, Notebooks, Lakehouse, and Data Pipelines.
• Implement Medallion architecture Design and maintain structured layers: Bronze (raw), Silver (cleansed/standardized), and Gold (business-ready).
• Model reusable datasets Develop scalable data models that support cross-domain analytics, multi-source comparisons, and machine learning (ML) consumption.

2. Advanced Analytics & Predictive Modeling

• Build ML models Develop machine learning models to identify trends, predict outcomes (e.g., asset performance, leasing risk, market movements), and detect anomalies.
• Perform advanced statistical analysis Execute time-series modeling, multivariate analysis, and scenario modeling.
• Translate business questions Convert ambiguous business problems into structured analytical frameworks and predictive models.

3. Insight Generation & Business Impact

• Extract actionable value Identify performance drivers, market opportunities, and risk signals from combined data sources.
• Deliver data products Create insight-ready Gold-layer datasets optimized for Power BI, downstream apps, and AI consumption.
• Communicate complex narratives Present technical findings to non-technical business stakeholders using clear, contextual storytelling.

4. MLOps & Productionization

• Manage ML lifecycle Handle end-to-end model training, validation, hyperparameter tuning, deployment, and performance monitoring.
• Embed production pipelines Integrate models into Fabric pipelines for automated batch and scheduled inference.
• Ensure scalability Build reliable, reusable, and scalable models that serve multiple enterprise use cases.

5. Data Governance & Quality

• Ensure data integrity Maintain high quality, consistency, and lineage across disparate external and internal data sources.
• Prioritize explainability Build transparent models with clear assumptions, drivers, and traceability.
• Align with standards Strictly adhere to enterprise data governance, privacy, and security policies.

6. Collaboration & Delivery

• Partner across teams Work closely with the Delivery Manager on vendor coordination, Data Engineers on pipeline architecture, and Business Leaders on use-case definitions.
• Drive outcomes Convert complex data discoveries into strategic recommendations that drive measurable business value.
• (Optional) Support AI enablement Contribute to future AI-driven workflows, insight automation, and smart recommendation engines (Note: Primary focus is data and modeling, not AI agent development).

Required Qualifications

Experience

• Industry Experience 5–8+ years of professional experience in Data Science, Machine Learning, or Advanced Analytics.
• Proven Track Record Demonstrated experience working with large, multi-source datasets and deploying production-grade models.

Technical Skills

• Core Languages Expert proficiency in Python (Pandas, PySpark, scikit-learn, etc.) and advanced SQL.
• Data Engineering Strong hands-on experience with data transformation, modeling principles, and ETL/ELT pipelines.
• Modern Platforms Direct experience working with Microsoft Fabric (or equivalent modern cloud data platforms like Databricks or Snowflake).
• Architecture Solid understanding of Medallion architecture and data warehousing concepts.

Analytical & Business Skills

• Methodologies Practical expertise in regression, classification, clustering, and time-series forecasting.
• Problem-Solving Ability to extract clear signals from complex, noisy, and unstructured datasets.
• Communication Strong capability to bridge the gap between technical execution and business strategy.

Nice-to-Have Qualifications

• Experience with real estate, investment management, or property tech datasets.
• Direct experience handling data feeds from external providers like CoStar, JLL, or CBRE.
• Exposure to Azure AI Foundry, LLM-based solutions, RAG architectures, or advanced AI workflows.
• Experience building and scaling end-to-end data products from the ground up.