Principle Data Scientist

**HIRING – FULLTIME PERMANENT – HYBRID TORONTO (Applicants in other parts of Canada are still welcome)**

Our online marketplace client is looking for a Principle Data Scientist with extensive experience in the SaaS environment. Candidates must have background in software engineering.

If you are interested and fit the requirements, please send your resume to safao@corgta.com! No third party vendors please

What You’ll Need
Advanced academic background in Computer Science, Engineering, Mathematics, or a related discipline.
10+ years of experience in data science, machine learning, or applied AI, with a track record of delivering production-ready, high-impact solutions.
Expert programming skills in Python and SQL, with strong proficiency in major ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
Direct experience working with GenAI/LLMs, including frameworks such as Hugging Face, LangChain, OpenAI APIs, vector databases, and model-fine-tuning techniques.
Deep understanding of machine learning methods—including supervised/unsupervised learning, deep learning, model evaluation, and explainability.
Strong hands-on experience with cloud platforms (AWS), containerization (Docker), and orchestration tools (e.g., Jenkins, Airflow).
Proven experience with MLOps practices, including CI/CD, model monitoring, versioning, and automated retraining pipelines.
Experience deploying and serving models via APIs (e.g., Flask, FastAPI) for real-time or batch inference.
Exceptional communication skills and the ability to translate complex technical concepts into actionable insights for non-technical audiences.
Demonstrated leadership experience in mentoring teams, setting technical direction, and championing best practices in reproducibility, experimentation, and responsible AI.
Experience working in Agile product environments (Scrum/Kanban); influencing product roadmaps is an asset.

Desirable Experience
Background in large-scale consumer platforms or data-rich digital environments.
Familiarity with product-driven data applications such as pricing, recommendations, forecasting, or optimization.
Contributions to open-source AI/ML communities, or publications/presentations at industry events.