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Montréal, Quebec, Canada
Actively hiring, first seen by us 2 days agoDesign evaluation datasets, rubrics, LLM-as-judge and deterministic scorers
Own the lifecycle of evaluation datasets and scorecards, including versioning and lineage
Design evaluation approaches for generative AI and agentic systems
Establish human evaluation protocols that produce reliable, audit-ready labels
Embed evaluations into build, release, certification and monitoring workflows
Design and drive advanced model and agent evaluation methodologies, including evaluation datasets, rubrics, LLM-as-judge methods, deterministic scorers
Own the end-to-end lifecycle for evaluation datasets and scorecards, including sourcing, curation, validation, quality checks, versioning, lineage, reuse, and ongoing improvement.
Design scalable evaluation approaches for generative AI and agentic systems
Establish human evaluation and review protocols that produce reliable labels, reviewer guidance, adjudication processes, quality controls, and audit-ready evidence.
embed evaluations into build, release, certification, monitoring, and recertification workflows
- Publications, patents, open-source contributions, or industry work related to AI evaluation, ML quality, AI safety, applied research, or responsible AI. - Ph.D. or Master’s degree in a quantitative or technical field, such as Applied Mathematics, Engineering, Computer Science or a related computational discipline. What’s in it for you?
Actively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
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Day 2
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From 9,990 similar postings that came down. Coming down is not always a hire.
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- Practical experience with LLM evaluation methods such as LLM-as-judge, deterministic scoring, human evaluation, hallucination assessment, factuality assessment, safety evaluation, or model quality benchmarking. - Strong technical foundation in data science, statistics, machine learning, experimentation, data curation, Python, SQL, and modern AI/ML development practices. - Proven ability to translate governance, model risk, responsible AI, and business requirements into measurable controls, repeatable evaluation processes, and decision-ready evidence.
- Experience in financial services, regulated AI, model risk management, responsible AI, enterprise governance, or audit-ready evidence processes. - Familiarity with tools and platforms such as MLflow, Langfuse, LangSmith, OpenTelemetry, Grafana, CI/CD pipelines, or comparable evaluation and observability tooling. - Publications, patents, open-source contributions, or industry work related to AI evaluation, ML quality, AI safety, applied research, or responsible AI.
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We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
Job Description
What is the opportunity?
RBC’s AI Group is building trusted AI capabilities for the enterprise, and evaluation is one of the core controls that makes that possible. As Staff Data Scientist, AI Evaluations, you will be a senior technical leader in the data science function responsible for how RBC measures model and agent quality, safety, risk, and performance. You will design, build, and continuously improve evaluation datasets, sourcing methods, LLM judge design, deterministic scorers, human evaluation protocols, quality benchmarks, and measurement frameworks that help AI systems move from experimentation to production with evidence and control, while providing technical guidance and mentorship to other data scientists on the team.
What will you do?
What do you need to succeed?
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What’s in it for you?
We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.
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Job Skills
Big Data Analytics, Critical Thinking, Decision Making, Industry Knowledge, Machine Learning (ML), Results-Oriented, Software Engineering, Software Product DesignAdditional Job Details
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Our Employment Opportunities
At RBC, we are guided by living shared values of Client First, Integrity, Collaboration, Respect and Excellence and winning together as One RBC. We believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world. Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities. RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.
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Montréal, Quebec, Canada
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