Staff Data Scientist, Applied ML

Role and team

What you will do

• Build, improve, and maintain SignalGraph, including metric contracts, trustworthy catalogs, Neo4j and series-refresh pipelines, and investigation diagnostics.
• Build and evaluate RAG systems on the graph, owning retrieval quality, context management, and evaluation.
• Own production ML training, real-time serving, monitoring, drift detection, and retraining.
• Establish systematic model and LLM evaluation and regression testing.
• Drive MLOps and ML engineering standards, shape platform and feature-store roadmaps, review work, and mentor scientists.
• Partner with senior leaders and analytics, BI, and product teams; present findings, defend assumptions, and support decisions.
• Track AI/ML advances and translate them into shipped capabilities.

Required qualifications

• Production experience training, deploying, serving, and maintaining models used by real users.
• Strong statistics foundation and ability to reason about bias, variance, loss functions, and signal versus noise.
• Expert SQL and production-grade Python skills.
• Depth in deep learning, neural architectures, transformers/BERT-family models, RNN/CNN, ranking, and representation learning.
• Hands-on experience applying LLMs in production, including RAG and context management.
• Experience with custom or asymmetric loss functions and large-data production platforms, including Snowflake and Apache Airflow.
• Cloud infrastructure experience; AWS is strongly preferred, while GCP or Azure equivalents are considered.
• Strong communication and stakeholder alignment skills, including influencing without authority.

Preferred qualifications

• Graph theory, graph neural networks, knowledge graphs, or graph databases such as Neo4j are particularly valuable.
• Software engineering experience with services or model serving, REST/gRPC, Docker/Kubernetes, CI/CD, or feature stores.
• Snowpark, Snowpark Container Services, or a comparable warehouse-to-deployed-model approach.
• LLM evaluation infrastructure, safety layers, or LLMOps for customer-facing AI.
• Exposure to risk, fraud, fintech modeling, recommendation, or ranking systems at scale.

Compensation

• Annual base salary range is CAD 145,900 to CAD 197,400, with a midpoint of CAD 171,600.
• The posting describes equity, health and wellness stipends, retirement savings matching, and extended health benefits as parts of total compensation.

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