Sr Data Scientist, AI Products
Flexibility to work remotely across CanadaFull-TimeSenior
SalaryCA$151,000 – CA$189,000 base salary, plus equity.
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonMachine LearningProduct AnalyticsData scienceA/B testing
Requirements
- 5+ years of experience in data science, machine learning, or AI product development roles.
- Strong experience building and deploying production machine learning models in real-world environments.
- Deep understanding of modern AI concepts including transformers, embeddings, attention mechanisms, and LLM architectures.
- Experience with model fine-tuning techniques such as LoRA, adapters, instruction tuning, or quantization.
- Strong product sense with the ability to define KPIs, design experiments, and translate business problems into measurable hypotheses.
- Experience with A/B testing, causal inference, and product analytics to drive data-informed decisions.
- Ability to collaborate effectively with cross-functional teams and communicate complex technical concepts clearly.
- Strong programming skills in Python and familiarity with production ML pipelines and data infrastructure.
- Experience with recommender systems, representation learning, or large-scale AI systems is considered an asset.
Responsibilities
- Lead the end-to-end AI product lifecycle, including problem definition, KPI design, model development, deployment, and impact measurement.
- Design and execute experiments such as A/B testing and causal inference studies to evaluate AI-driven features.
- Build, train, and maintain production-grade machine learning and AI models, including transformer-based and LLM systems.
- Develop scalable, reproducible data and model pipelines with strong engineering best practices.
- Analyze production data and user behavior to iterate and improve model performance and product outcomes.
- Partner with Product, Engineering, and Design teams to translate business needs into AI/ML solutions and technical roadmaps.
- Contribute to the design of evaluation frameworks, model monitoring systems, and experiment infrastructure.
- Mentor team members and contribute to a strong culture of collaboration, research excellence, and product thinking.
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