​Job Description – Data Scientist (AI + Data Engineering | External Data Intelligence)

Job Title​ - Data Scientist (AI + Data Engineering) – External Data & Intelligent Insights


Location​ - Toronto, ON (Hybrid)


Role Summary
We are seeking a Data Scientist with strong Data Engineering capabilities to build advanced analytical​ models and insights by combining external market data (CBRE, JLL, CoStar, etc.) with internal enterprise​ datasets within our Microsoft Fabric-based Data Platform.


This role focuses on:
• Integrating multi-source external data
• Engineering governed, reusable datasets
• Developing predictive and analytical models
• Generating actionable insights for business and downstream applications


The ideal candidate will operate across the full lifecycle:
data ingestion → transformation → modeling → insight generation, and help unlock high-value intelligence​ across enterprise use cases.


Key Responsibilities
1. Data Integration & Engineering (Hands-On)
• Ingest and integrate external vendor datasets (market, macro, demographic, rental data)
• Combine external data with internal enterprise sources (Yardi, asset management, investments)
• Build pipelines using Microsoft Fabric:
o Dataflows, Notebooks, Lakehouse, Data Pipelines
• Implement Medallion architecture:
o Bronze: Raw ingestion
o Silver: Cleansed and standardized datasets
o Gold: Business-ready analytical datasets
• Develop reusable data models to support:
o Cross-domain analytics
o Multi-source comparisons
o ML model consumption


2. Data Science & Advanced Analytics
• Build machine learning models to:
o Identify trends, correlations, and patterns
o Predict key outcomes (e.g., asset performance, leasing risk, market movement)
o Detect anomalies and opportunities
• Perform advanced analysis:
o Time-series modeling
o Multivariate analysis
o Scenario modeling
• Translate business questions into:
o Analytical frameworks

o Predictive models
o Data-driven insights
3. Insight Generation & Business Impact
• Generate actionable insights from combined internal + external data:
o Performance drivers
o Market opportunities
o Risk signals
• Develop insight-ready datasets (Gold layer) for:
o Reporting (Power BI / apps)
o AI-driven consumption
o Decision support
• Clearly communicate findings to business stakeholders with strong narrative and context


4. (Optional Exposure) AI & Advanced Analytics Enablement
• Contribute to enabling AI-driven use cases such as:
o Insight automation
o Data-driven recommendations
o Advanced analytics workflows
• Support integration of analytical outputs into downstream applications or intelligent systems
(Note: Primary focus remains on data + modeling, not agent development)


5. MLOps & Productionization
• Implement end-to-end ML lifecycle:
o Model training, validation, and tuning
o Deployment and monitoring
• Integrate models into production pipelines within Fabric:
o Batch and scheduled inference
• Ensure models are:
o Scalable
o Reliable
o Reusable across use cases


6. Data Quality, Governance & Explainability
• Ensure high-quality and consistent datasets across sources
• Build explainable models:
o Clear assumptions and drivers
o Traceability to source data
• Align with enterprise governance standards


7. Collaboration & Delivery
• Work closely with:
o Delivery Manager (external data sourcing & vendor coordination)
o Data Engineers (pipeline build)
o Business stakeholders (use case definition)
• Translate complex analysis into:

o Actionable business insights
o Clear recommendations

Required Qualifications
Experience
• 5–8+ years of experience in:
o Data Science / Machine Learning / Advanced Analytics
• Proven experience:
o Working with large, multi-source datasets
o Delivering production-grade models

Technical Skills
• Strong proficiency in:
o Python (pandas, PySpark, ML libraries)
o SQL
• Hands-on experience with:
o Data transformation and modeling
o Data engineering concepts and pipelines
• Experience with Microsoft Fabric or similar modern data platforms
• Strong understanding of:
o Medallion architecture
o Data modeling principles

Modeling & Analytics
• Experience with:
o Regression, classification, clustering
o Time-series forecasting
o Statistical analysis
• Strong problem-solving mindset with ability to extract signal from complex data
Business Skills
• Ability to translate:
o Business problems → analytical models → actionable insights
• Strong communication skills:
o Able to explain insights to non-technical stakeholders

Nice-to-Have
• Experience with:
o Real estate / investment / market datasets
o External data providers (CBRE, JLL, CoStar, etc.)
• Exposure to:
o Azure AI Foundry, AI/ML platforms, or LLM-based solutions
o AI agents, RAG architectures, or advanced AI workflows
• Experience building:
o End-to-end data + analytics products