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1 result for Data Lead in Toronto, ON

Data Scientist
  • North York, ON
  • onsite
  • Permanent
  • 100000 - 120000 CAD / Yearly
  • <p><strong><u>This job posting is for a current vacancy with our client.</u></strong></p><p><br></p><p>We are seeking an experienced Data Scientist for our client&#39;s growing Analytics team. The Data Scientist will be based in Toronto, Ontario, where they will turn complex data into practical insights that support business decisions. </p><p><br></p><p>A core focus of this role includes machine learning expertise, data engineering capability, and analytical thinking to develop scalable solutions in a Databricks environment. You will work closely with technical and business teams to build reliable models, improve data workflows, and communicate findings in a clear and meaningful way.</p><p><br></p><p>Key Responsibilities:</p><p>• Develop, implement, and operationalize machine learning models and analytical solutions within Databricks to address business needs.</p><p>• Create and support scalable data pipelines using Apache Spark, including PySpark or Scala, to process large and diverse datasets efficiently.</p><p>• Examine both structured and unstructured data sources to identify trends, generate forecasts, and support data-driven decision-making.</p><p>• Partner with engineers, analysts, and business stakeholders to define objectives and translate them into practical data science solutions.</p><p>• Use Lakehouse principles in Databricks to manage data ingestion, transformation, storage, and model deployment in a streamlined manner.</p><p>• Apply disciplined machine learning practices across feature creation, model training, testing, deployment, and ongoing performance monitoring.</p><p>• Produce dashboards, visual summaries, and reports that present analytical results clearly for technical and non-technical audiences.</p><p>• Maintain strong standards for data accuracy, governance, security, and compliance throughout analytics and machine learning workflows.</p><p>• Improve the performance, scalability, and cost effectiveness of Databricks processing and model execution environments.</p>
  • 2026-06-07T00:00:00Z