Applied Data Scientist, Finance AI Evaluation & Datasets

5+ years of data science experience, with at least 2+ years in financial services, fintech, banking, or a comparable regulated data environment.

Real working knowledge of financial data and workflows: financial statements, SEC filings, transaction data, and other common financial-services document types.

Hands-on experience with unstructured and multimodal financial data — some combination of PDFs, scanned documents, spreadsheets, charts, or call transcripts.

Familiarity with financial standards or protocols such as XBRL, ISO 20022, or GAAP/IFRS reporting concepts, etc. is strongly preferred.

Hands-on experience designing datasets for ML — not just consuming them. You have written annotation guidelines, sized cohorts, set quality thresholds, and shipped data that downstream teams could actually train, evaluate, or monitor on.

Familiarity with LLM-based and multimodal financial AI workflows: prompt design, rubric-based evaluation, RAG, LLM-as-judge methods, and the limitations of automated evaluation in high-stakes contexts.

Strong Python and SQL; comfort with pandas, scikit-learn, or equivalent; working familiarity with Hugging Face, PyTorch, or model APIs.

Statistical literacy: sampling design, inter-annotator agreement metrics (e.g., Cohen's kappa), confidence intervals, and the ability to push back when a number is being over-interpreted.

Solid grasp of financial services privacy, compliance, and governance: PII handling, GLBA or equivalent privacy regimes, MNPI sensitivity, and documentation fit for regulated AI programs.

Excellent collaboration skills — upstream with a Technical Solutions Architect, sideways with research scientists and engineers, and downstream with SME annotators and quality teams.

A bias toward financial workflow realism. You would rather build a smaller dataset that reflects what analysts, advisors, or customers actually see than a larger one that looks impressive on paper but fails in practice.

Degree in a relevant field — statistics, data science, economics, finance, or a related quantitative field, or equivalent demonstrated experience. Formal finance credentials aren't required, but CFA, FRM, or MBA backgrounds, etc. are especially encouraged.

Experience designing evaluations for LLMs, VLMs, or multimodal models in financial reasoning, filings analysis, or fraud/AML contexts.

Experience with document AI, OCR/post-OCR quality, or table and chart extraction for complex financial documents.

Familiarity with agentic evaluation, AI observability, experiment tracking, or tools such as Weights & Biases or LangFuse.

Familiarity with model risk management frameworks, validation documentation, fairness/bias auditing, or consumer protection analysis.

Experience with multilingual or cross-border financial data, or published/open-source work in financial AI or model governance.

The expected salary range for this position is $210,000 – $245,000 CAD per year, based on experience, skills, and qualifications.

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