Applied Data Scientist, Finance AI Evaluation & Datasets

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,