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Geospatial Data Science Professional
2 months ago
About QuadReal Property Group
QuadReal Property Group is a global real estate investment, operating and development company headquartered in Vancouver, British Columbia. Its assets under management total $73.8 billion. From its foundation in Canada as a full-service real estate operating company, QuadReal has expanded its capabilities to invest in equity and debt in both the public and private markets. QuadReal invests directly, via programmatic partnerships and through operating companies in which it holds an ownership interest.
Company Overview
QuadReal seeks to deliver strong investment returns while creating sustainable environments that bring value to the people and communities it serves. Now and for generations to come.
Role Description
We are continuing to grow our Enterprise Data & Analytics (ED&A) team at QuadReal. The ideal candidate would be someone who wants to build, improve, and incorporate technologies that make the lives of our community more enriched, and who is motivated by delivering customer value.
Job Summary
The Data Scientist in the Enterprise Data & Analytics Group will play a pivotal role in helping revolutionize the real estate industry through the development and implementation of advanced data-driven solutions. This position involves building state-of-the-art geospatial, time series, and spatiotemporal models, as well as integrating Generative AI (GenAI) technologies to enhance analytical capabilities and drive innovation.
Key Responsibilities
- Machine Learning POV Development: Design and build Machine Learning-based user-facing proof-of-value tools to showcase advanced analytics and data science capabilities, focusing on spatial and/or temporal data.
- Spatial and Temporal Analysis: Apply spatial and temporal analysis techniques to derive actionable insights that drive business decisions and innovation.
- ML Engineering: Develop and implement machine learning models and pipelines, ensuring they are scalable, maintainable, and performant.
- End-to-End ML Lifecycle Management: Oversee the entire machine learning lifecycle, from data preparation and model training to deployment and monitoring.
- Cross-Functional Collaboration: Collaborate with analytics engineers, data engineers, and other stakeholders to integrate data science solutions into existing systems.
- Mentorship: Mentor junior engineers and foster a culture of continuous learning and improvement within the data science team.
- Stakeholder Engagement: Explain complex machine learning concepts and insights to non-technical business users, ensuring alignment and understanding across the organization.
- Industry Expertise: Leverage experience in real estate or related industries that require spatial data science to enhance our analytics capabilities.
- Tech Stack Evolution: Adapt to the continuous evolution of technology stacks, incorporating new tools and methodologies to improve data science workflows.
- External Contractor Management: Work closely with external contractor data science teams to ensure alignment with internal goals and maintain oversight of deliverables.
Qualifications
- Master's degree or PhD in Data Science, Computer Science, Statistics, or a related field.
- Proven experience (3-7 years) in software engineering and machine learning engineering, with a focus on building user-facing applications.
- Strong expertise in spatial and temporal data science, with experience applying these skills in real-world scenarios.
- Proficiency in machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn) and programming languages (e.g., Python, R).
- Experience in real estate or industries requiring spatial data science is highly desirable.
- Excellent communication skills, with the ability to explain complex concepts to non-technical audiences.
- Demonstrated ability to mentor and develop junior team members.
- Experience working in environments with continuously evolving tech stacks.
Key Relationships
- Reports to SVP, Enterprise Data & Analytics (ED&A)
- Business Leaders in various functions
- Enterprise Data & Analytics and IT teams
