Data Scientist (Remote)

Altus Group; Saskatoon; Remote; CAD 80,000 - 100,000; Full time

Job summary

A leading analytics firm in Canada is seeking a Data Scientist to develop innovative data solutions for commercial real estate. The ideal candidate will have a strong foundation in statistics, proficiency in SQL and Python, and experience with data science methodologies. You will collaborate with various stakeholders, conduct experiments, and communicate findings to support data-driven decisions. This role is ideal for someone passionate about analytics and teamwork.

Qualifications

• 2+ years of experience in data science or applied economics.
• Proficiency in SQL and Python.
• Experience working in cloud environments.

Responsibilities

• Data wrangling and feature engineering.
• Data-driven planning and modeling with stakeholders.
• Rapid prototyping of analytics solutions.
• Conducting data science experimentation and testing.

Skills

Statistics / Econometrics

SQL

Python

Data ETL

Feature Engineering

Machine Learning

Collaborative Communication

Education

BS or MS degree in Economics, Computer Science, Applied Mathematics

Tools

GitHub

Databricks

Job description

Job Summary :

Altus Group is seeking a Data Scientist to support Altus Labs, the firm's commercial real estate (CRE) hub for innovation, focused on applying new data, methodologies, and tech to drive better client investment and operating outcomes. As a Data Scientist, you will support the development of new analytics and data solutions through statistical modeling, feature engineering, machine learning, and AI. Our Data Scientists collaborate with a team of other Data Scientists, economists, and other technology and product stakeholders.

Key Responsibilities :

Data wrangling and feature engineering : Ideate and evaluate potential features and then apply data cleaning and transformation to organize messy raw data into usable features for analysis.

Data-driven planning and modeling : Work with key stakeholders to validate the key assumptions supported by data insights and quantify the risks and impact to make smart decisions about features, transformations, and models.

Rapid prototyping : Leverage innovative tools or approaches to quickly prototype what the success of the product could look like against the success criteria and iteratively review with key stakeholders for feedback which will then inform the next iterations.

Data science experimentation and testing : Exploratory data analysis, feature selection, model selection, hyperparameter tuning, model evaluation, model validation and monitoring.

Setting standards : Collaborate with other Data Scientists, Machine Learning Engineers, Operations Managers, and other stakeholders to explore and implement processes and tools to improve data science quality and efficiency.

Ideation and brainstorming discussion : Show curiosity, ask discovery questions, avoid premature conclusions, flag risks and concerns at early stages, and facilitate trade-off or prioritization discussions.

Communication and reporting : Translating and communicating complex data science concepts and the value of analytical projects to non-technical stakeholders. Creating lightweight prototype demos, preparing and presenting reports, findings, and recommendations to senior management and other key stakeholders to support go / no-go recommendations.

Documenting best practices : Establishing new modeling approaches, pipelines and infrastructure as part of the Data & Analytics team at Altus by sharing and documenting best practices based on solid statistical / econometric / ML principles.

Key Qualifications :

A collaborative communicator who can work independently and within a team to share insights and model approaches.

Passionate about succeeding together and scaling your impact through collaboration.

A scientist at heart—methodical, curious, and driven by thoughtful experimentation.

Strong background in statistics / econometrics with familiarity in hypothesis testing, regression models, time series analysis, tree-based models, clustering, anomaly detection, etc. (Bonus : experience in NLP, transfer learning, or Generative AI.)

Programming proficiency in SQL and Python with experience across the model lifecycle : data ETL, feature engineering, model tuning, evaluation, and deployment.

Comfortable brainstorming, receiving feedback, and iterating on solutions with a growth mindset.

2+ years of experience in data science or applied economics, depending on academic background.

BS or MS degree in Economics, Computer Science, Applied Mathematics, or a related technical field.

Experience with GitHub, version control, and collaborative coding workflows.

Skilled at writing clean, efficient, and maintainable code with attention to performance and cost.

Experience working in cloud environments and handling large, complex datasets.

Prior experience with Databricks (Catalog, Workflows, Experiments, Materialized Views) is a plus.

Commercial real estate experience is a plus.

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