Job Description & How to Apply Below
As a principal data scientist, you will bridge the gap between complex business requirements, modern cloud data architectures, and predictive machine learning models. Operating within a hybrid work model, you will lead the design and deployment of AI/ML models, build scalable ETL pipelines, process large-scale datasets using cloud platforms, and create intuitive business intelligence dashboards. This position demands an analytics expert who can leverage machine learning algorithms, natural language processing (NLP), and deep learning techniques to drive intelligent automation and data-informed decision-making.
Location:
Toronto, ON
Assignment Type:
Hybrid (Minimum 2 days per week onsite)
Contract Duration: 52 weeks (12 months)
Advantages High-Impact AI/ML Scope:
Architect and deploy advanced predictive models, NLP algorithms, and intelligent automation solutions.
Modern Azure Cloud Ecosystem:
Deepen technical expertise using Azure Databricks, Azure Synapse, Azure Machine Learning (AML), and Azure OpenAI services.
Large-Scale Data Analytics:
Process and transform high-volume structured and unstructured datasets using Apache Spark and Python.
Flexible Hybrid Environment:
Work within a collaborative hybrid arrangement combining onsite teamwork in Toronto with remote flexibility.
Responsibilities Collaborate with cross-functional teams to identify key operational needs, conduct exploratory data analysis, and implement scalable, data-driven solutions.
Design, build, deploy, and optimize predictive machine learning, deep learning, and AI models using Azure Machine Learning (AML) and Azure OpenAI.
Construct and maintain robust ETL pipelines, processing and transforming large-scale datasets using Azure Databricks, Azure Synapse, and Apache Spark.
Perform feature engineering, statistical modeling, data mining, and predictive analytics to extract actionable business insights.
Design and publish interactive reports, executive data visualizations, and dashboards using Power BI and modern analytics tooling.
Enforce best practices in data governance, model performance monitoring, scalable cloud architecture, and data virtualization.
Support all phases of testing (Systems Integration Testing, QA, UAT) by validating model accuracy, tuning performance, and resolving technical defects.
Author comprehensive technical design specifications, functional documentation, presentation decks, and status reports to support operational transition.
Qualifications Core Technical & Data Science Requirements
Data Science & ML Tenure: 5+ years of progressive experience in data science, predictive modeling, machine learning, and advanced analytics.
Azure Cloud Data Stack: 5+ years of hands-on experience utilizing Azure SQL Server, Azure Synapse Analytics, and Azure Databricks.
Programming Mastery: 5+ years of hands-on experience writing complex data manipulation and analytics code in Python and SQL.
AI/ML Model Deployment:
Proven experience building and deploying AI/ML solutions using Azure Machine Learning (AML) and Azure OpenAI services.
Big Data Processing:
Expertise with Apache Spark, data lake architectures, dimensional modeling, and processing large-scale structured and unstructured data.
Data Visualization:
Hands-on expertise creating executive dashboards, semantic models, and reporting views using Power BI or similar tools.
Preferred Assets &…
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