Hybrid Data Scientist – Toronto | ML, Python & Azure

Indotronix UK

Toronto

Hybrid

CAD 90,000 - 130,000

Full time

13 hours ago
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Job summary

Cloud Resourcing is seeking a Data Analytical Specialist/Scientist for a hybrid role in Toronto, ON. The candidate will leverage deep statistical knowledge and hands-on ML with Python/SQL to build, validate and tune models on real-world datasets, using tools like Power BI, Databricks, and Azure Foundry.

The ideal candidate has extensive experience (10–15 years) and will work in a hybrid setup to deliver data-driven insights and scalable analytics solutions.

Qualifications

  • Deep expertise in statistics, probability, predictive modelling and machine learning including classification, regression, clustering, feature engineering, experimentation, model selection, validation, bias/variance, overfitting and inference.
  • Advanced hands-on proficiency in Python, SQL and ML frameworks, with demonstrated experience building, tuning and validating models using real-world datasets. Tech stack: Python, SQL, Power BI, VSCode, Git, Jira, Azure (OpenAI, Foundry and ML Studio), Databricks.
  • Strong hands-on experience with Azure Machine Learning and Microsoft Foundry/Azure AI Foundry.

Responsibilities

  • Develop and validate predictive models on real-world datasets.
  • Tune and validate models using experimentation and real-world data.
  • Create data visualizations and dashboards (Power BI) to communicate insights.
  • Collaborate with cross-functional teams in Azure ecosystem.

Skills

Statistics/ML concepts
Python & SQL
Azure ML Foundry

Tools

Python
SQL
Power BI
VSCode
Git
Jira
Azure AI Foundry
Databricks

Job description

Cloud Resourcing is seeking a Data Analytical Specialist/Scientist for a hybrid role in Toronto, ON. The candidate will leverage deep statistical knowledge and hands-on ML with Python/SQL to build, validate and tune models on real-world datasets, using tools like Power BI, Databricks, and Azure Foundry.

The ideal candidate has extensive experience (10–15 years) and will work in a hybrid setup to deliver data-driven insights and scalable analytics solutions.

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