Hiring: Applied Data Scientist, Finance AI Evaluation & Datasets Company: Innodata Inc. Location: United States Job Posted Time: 2026-09-10 12:53:12 Target Skills & Keywords : AI, CFA, Hugging Face, LLM, Pandas, PyTorch, Python, RAG, Regulatory Compliance, Risk Management, SQL, scikit-learn About the job Experience: •36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. •5+ years of data science experience, with at least 2+ years in financial services, fintech, banking, or a comparable regulated data environment. Required Skills: •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. Compensation: •$150,000 - $175,000 / year Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!