Lead Data Scientist
New
CanadaFull-TimeLead
Salary160,000 - 200,000 USD per year
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Job Details
- Experience
- 6+ years
- Required Skills
- DockerPythonSQLMachine LearningMLFlowPyTorchscikit-learn
Requirements
- 6+ years of hands-on machine learning experience with a proven track record of deploying models into production environments.
- Strong experience in client-facing or consulting roles, with the ability to manage expectations, scope, and delivery risk effectively.
- Deep expertise in at least one ML domain such as predictive analytics, computer vision, or LLM/agent-based systems.
- Strong Python and SQL skills, with experience building production-grade code using frameworks such as PyTorch, Scikit-learn, or Hugging Face.
- Experience working across full ML deployment stacks, including cloud services, Docker, and infrastructure-level development.
- Familiarity with ML lifecycle tools such as MLflow, Weights & Biases, or similar platforms.
- Strong communication skills with the ability to explain complex models to both technical and non-technical stakeholders.
- Advanced degree in a quantitative field (or equivalent practical experience through shipped production work).
Responsibilities
- Lead end-to-end client engagements, including problem scoping, solution design, delivery execution, and stakeholder management.
- Design, build, and deploy machine learning solutions across diverse domains, ensuring scalability, reliability, and business impact.
- Manage and mentor small teams of data scientists, providing technical guidance and supporting professional development.
- Act as the primary technical and strategic point of contact for clients, ensuring alignment and clear communication throughout delivery.
- Develop production-grade ML systems using cloud platforms (e.g., SageMaker, Vertex AI, Azure ML) and Python-based infrastructure.
- Evaluate and integrate emerging tools, frameworks, and methodologies to improve solution quality and delivery efficiency.
- Contribute to best practices in ML engineering, experimentation, and lifecycle management across projects.
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