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
