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Added 8 hours ago
Analyst, Data Insights & Business Transformation (12 Months Fixed Term Contract/Secondment)
CIBC
Toronto, ON
Full-time
…collection, validation, and maintenance of client data to support AML/KYC, regulatory, and operational requirements. As the Analyst, Data Insights & Business Transformation, reporting to…
Added 9 hours ago
Home Financing Officer - Prairies
Scotiabank
Winnipeg, MB
…(RESL) business in Canada by supporting mortgage-related fraud, AML, and reputational risk reviews across branch, broker, and Home Financing Advisor (HFA) channels. The Senior Analyst…
Added 12 hours ago
Client Scam Intervention Specialist
Scotiabank
Toronto, ON · Scarborough, ON
…with clients who may be under the influence of a scammer. The analyst uses empathy, active listening, sound judgment, and persuasive communication to help clients recognize manipulation,…
Added 4 days ago
Anti-Money Laundering Investigator
Swoon
Toronto, ON
Contract
Experience with AML, transaction monitoring, investigation, or case management platforms is an asset.
Added 1 month ago
Analyst, Derivative Settlement Operations (12-Month Contract GWO)
Scotiabank
Toronto, ON
Responsible for day-to-day operational activities. This includes but is not limited to completing daily settlements and investigation/resolution of settlement disputes with…
Added 25 days ago
AML Subject Matter Expert (contract)
Capgemini
Toronto, ON
Contract
Leverage strong Business Systems Analyst (BSA) experience within the AML domain to support compliance initiatives and system enhancements.
Added 22 days ago
Financial Crime Risk Investigative Analyst
TD
Toronto, ON
Full-time
The AML Financial Crime Risk Investigative Analyst provides AML analytical and/or operations process support within a defined area. As a key resource and specialist, you will build and…
Added 19 days ago
AML & Compliance Analyst
Heartwood Financial Group
Toronto, ON · North York, ON
Full-time
The AML & Compliance Analyst will be responsible for supporting the day-to-day execution of Heartwood's AML/ATF compliance program and assisting with the identification, assessment,…
Added 20 days ago
Financial Crime Risk Analyst II (4936, 4938)
TD
Markham, ON · Toronto, ON
Full-time
The Quality Assurance (QA) Analyst is primarily responsible for executing independent QA reviews over TD Securities Monitoring and Investigations (TDSMI) AML/ATF activity to assess…
Added 20 days ago
Senior Financial Crime Risk Analyst (4818)
TD
Markham, ON · Toronto, ON
Full-time
The Senior Quality Assurance Analyst, is responsible for independently performing complex financial crime risk reviews, quality assurance activities, effective challenge, second-level…
Added 1 month ago
Compliance Analyst –Chainalysis SME
Xace
Canada
Full-time
We are seeking a detail-oriented and commercially minded Compliance Analyst to join our growing Compliance team in Canada.
Added 1 month ago
Compliance Analyst – Sumsub SME
Xace
Canada
Full-time
We are seeking a detail-oriented and commercially minded Compliance Analyst to join our growing Compliance team in Canada.
Added 1 month ago
Risk Analyst
Relay
Toronto, ON
Full-time
Working knowledge of compliance: BSA/AML, KYC/CIP, and how regulations shape monitoring decisions
Added 1 month ago
Options Control Analyst
Monex Global
Toronto, ON
Full-time
The Options Control Analyst is responsible for executing critical operational controls across Monex's global structured FX options business, ensuring the accurate capture, validation,…
Added 1 month ago
Risk Analyst
Relay
Remote, Canada
Full-time · Remote
Working knowledge of compliance: BSA/AML, KYC/CIP, and how regulations shape monitoring decisions
Added 1 month ago
Analyst, AML & Sanctions Compliance
Toromont Cat
Concord, ON
Full-time
Toromont Industries Ltd. (“Toromont”) is looking to hire a permanent, full-time Analyst, AML & Sanctions Compliance, who will support the ongoing development, monitoring, and execution of…
Added 1 month ago
Options Control Analyst
Monex USA
Toronto, ON
Full-time
The Options Control Analyst is responsible for executing critical operational controls across Monex's global structured FX options business, ensuring the accurate capture, validation,…
Added 17 days ago
Staff Software Engineer, Financial Crimes
Stripe
Toronto, ON
…time, and automating signal acquisition and synthesis into investigation workflows. We're now re-architecting our screening platform to make it safe and easy for new Stripe products to…
Added 1 month ago
Sr. Analyst, AML - Neurodiversity Hiring Initiative
Specialisterne
Toronto, ON
Full-time
You’ll be joining our Financial Intelligence Unit (FIU) Anti-Money Laundering (AML) Investigations Team. You’ll provide timely and proactive advice and independent oversight of CIBC’s…
Added 1 month ago
AML Risk Analyst
Peoples Group
Vancouver, BC
Full-time
The AML Risk Analyst is a highly motivated, proactive professional who thinks outside the box and takes initiative without waiting for direction. This individual is hands-on, willing to…
Added 11 days ago
Analyst, Derivative Settlements, Settlements and Clearing (GWO 1 Year Contract)
Scotiabank
Toronto, ON
Responsible for day-to-day operational activities. This includes but is not limited to completing daily settlements and investigation/resolution of settlement disputes with…
Added 11 days ago
Anti-Money Laundering Analyst
SPECTRAFORCE
Toronto, ON · Greater Toronto Area, Canada
Contract
Perform quality assurance reviews across a broad range of money laundering investigation activities in support of Anti-Money Laundering (AML) and Anti-Terrorist Financing (ATF) requirements…

No more jobs.

Closed listing

Innodata Inc.

Applied Data Scientist, Finance AI Evaluation & Datasets

Canada

Pay: $210,000 to $245,000 CAD / year
Job type: Full-time
Experience: 5+ years of data science experience, with at least 2+ years in financial services, fintech, banking, or a comparable regulated data environment.
Posting language: English
Posted: Jul 24, 2026

This opening has closed.

The original listing is kept below for reference.

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Original job description

Archived listing. The details below describe a past opening.

Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.

Scope Of The Role

Financial services is one of the highest-stakes domains for generative AI. Numerical accuracy, regulatory compliance, model risk management, auditability, and customer harm prevention, among other concerns, are the bar for shipping anything real. Innodata partners with foundation model labs, banks, asset managers, fintechs, and other enterprise AI teams building LLMs, multimodal systems, and AI agents for financial workflows.

As an Applied Data Scientist, Financial AI Evaluation & Datasets, you own the design, measurement quality, and domain validity of the datasets used to train, fine-tune, evaluate, and monitor financial-domain LLMs, vision-language models, multimodal document models, and AI agents. You bring financial-domain fluency and data science rigor: you can read a risk policy, financial statement, or customer transcript, among other financial-services documents; turn it into a measurable dataset and evaluation specification; define what correct, grounded, compliant, and safe mean for the use case; and produce evidence that sophisticated financial-services customers, model-risk teams, and AI governance stakeholders can trust.

This role has a special emphasis on unstructured and multimodal financial data - PDFs, scanned documents, spreadsheets, charts, call transcripts, and other mixed-document workflows where text, numbers, visuals, and metadata all matter. You will work in a pod with a Technical Solutions Architect (scopes the engagement), an Applied Research Scientist (shapes evaluation methodology), an AI/ML Research Engineer (builds training and evaluation infrastructure), and Language Data Scientists (run annotation at scale), making sure what the team produces is domain-valid, statistically defensible, compliant, auditable, and useful for evaluation and post-training.

What You’ll Own

Translate customer goals - such as improving financial reasoning, building an eval suite for earnings-call summarization, or evaluating an AML/fraud copilot - into concrete dataset specifications, taxonomies, rubrics, and acceptance criteria.
Design training and evaluation datasets across the financial AI surface: financial QA, filings and earnings analysis, credit and underwriting, fraud/AML investigation, and compliance, among other financial workflows.
Foreground unstructured and multimodal financial data in dataset design - PDFs, scanned statements, tables, charts, and call transcripts - used by analysts, advisors, compliance reviewers, and operations teams.
Design datasets and evaluations for retrieval-augmented and source-grounded systems: evidence citation and faithfulness to source documents, data freshness, conflict resolution across sources, and failure modes caused by incomplete or incorrectly parsed context.
Evaluate agentic and workflow-integrated financial AI systems: tool use, retrieval, transaction boundaries, escalation behavior, and controls that prevent unsafe or unauthorized actions.
Develop evaluation methodology that goes beyond surface accuracy - numerical consistency, hallucination rates on high-risk claims, refusal and escalation appropriateness, robustness under ambiguity, and fairness across protected or sensitive customer segments.
Define sampling strategies, label schemas, and adjudication workflows with Language Data Scientists and finance SMEs; write annotation guidelines that make subjective finance-domain judgments explicit, calibratable, and auditable.
Build the statistical and ML tooling that makes large financial datasets trustworthy: stratified sampling across products, markets, and modalities; bias analysis; leakage detection; and distribution shift checks, among other reliability checks.
Build evaluation and dataset-quality evidence to support financial-services model risk management: assumptions, limitations, validation results, and residual risks, packaged as reproducible evidence.
Partner with the AI/ML Research Engineer to instrument datasets into training, evaluation, and monitoring pipelines - rubric-grounded LLM-as-judge prompts, regression suites, and continuous monitoring.
Own data quality end-to-end, from intake through delivery: PII handling, provenance tracking, versioning, and modality-specific QA checks.
Reason about financial workflow context: where AI outputs enter analyst, advisor, compliance, risk, or customer-facing workflows; what evidence a reviewer needs to trust them; and when uncertainty must be surfaced.
Support the Technical Solutions Architect during customer discovery and proposals: scoping dataset programs, sizing annotation effort, and explaining methodology to client stakeholders.
Stay current on the financial AI landscape: regulatory developments, benchmark releases, and emerging evaluation methodology for finance-domain models.
Contribute to Innodata internal IP: reusable taxonomies, evaluation rubrics, golden datasets, and methodology templates.

You’ll Thrive In This Role If You Have

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.

Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams.

If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at [email protected] and consider reporting it to the FTC at ReportFraud.ftc.gov.

About employer

More about this job

Responsibilities

The role involves designing and validating high-quality datasets used to train and evaluate financial-domain LLMs and multimodal AI agents. You will translate complex financial goals into measurable specifications and build statistical tooling to ensure model reliability and regulatory compliance.

Requirements

Requires 5+ years of data science experience with at least 2 years in financial services and a strong proficiency in Python and SQL. Candidates should have hands-on experience designing ML datasets and a deep understanding of unstructured financial data and regulatory frameworks.

Skills

Data Science
Financial Analysis
LLM Evaluation
Python
SQL
Multimodal Data Analysis
RAG
Statistical Sampling
Model Risk Management
Dataset Design
Pandas
Scikit-learn
Hugging Face
PyTorch
Prompt Design
Compliance & Governance

Visa sponsorship

Not detected in the job text

Keywords

Generative AI
LLM
Financial Services
Multimodal Models
RAG
XBRL
ISO 20022
GAAP
IFRS
Model Risk Management
PII
GLBA
MNPI
Weights & Biases
LangFuse
OCR
Document AI
CFA
FRM
MBA
Python
SQL
PyTorch
Hugging Face
Scikit-learn
Pandas
Cohen's Kappa
Stratified Sampling
AI Governance
AML

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