Applied Data Scientist

Job Description

REQUIREMENTS

• 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.

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.
• 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 our client’s internal IP: reusable taxonomies, evaluation rubrics, golden datasets, and methodology templates.

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