Innodata Inc.
Applied Data Scientist, Finance AI Evaluation & Datasets
Remote - United States
Sponsorship not specifiedDetected 15 days ago
Machine LearningData EngineeringData ScienceLLMsAgentic AIComplianceResearchGAAPIFRSUnderwriting
About 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.
Responsibilities
- 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.
- 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.
- 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.
- Build the statistical and
- 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.
Requirements
- and when uncertainty must be surfaced.
Nice to have
- 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
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