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'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