Finance Research Technical Staff

What You'll Do9

• Create and maintain evaluation frameworks for AI agents in finance, covering benchmark suites, scoring methods, quality rubrics, and research-grade protocols.
• Carry out original studies on financial reasoning, decision-making, and workflow automation, then turn those findings into measurable gains in AI performance.
• Build and curate datasets, test cases, and benchmark environments that reflect real enterprise finance challenges.
• Work with AI researchers, machine learning engineers, and product teams to assess, validate, and refine finance-focused models and agentic systems.
• Examine model behavior, failure modes, and performance trends to produce insights that guide research priorities.
• Write internal research reports, technical documentation, and best practices that advance understanding of AI capabilities in finance.
• Track advances in AI, machine learning, financial modeling, evaluation science, and agent architectures, and bring emerging techniques into research work.
• Help set research standards and thought leadership for enterprise financial intelligence and AI evaluation.
• Support a culture of scientific rigor, experimentation, and collaborative innovation in a distributed research setting.

Requirements7

• Advanced degree in Finance, Economics, Financial Engineering, Quantitative Finance, or a related field. A PhD is strongly preferred; an MBA, CFA, or equivalent expertise also counts.
• Deep domain knowledge in at least one area of finance: investment research, capital markets, risk management, corporate finance, accounting, or financial analysis.
• Proven experience conducting rigorous research, developing analytical methodologies, or building evaluation frameworks in finance or a related quantitative discipline.
• Strong ability to turn ambiguous real-world financial problems into measurable research questions and evaluation criteria.
• Exceptional analytical and critical-thinking skills, with a record of solving complex, multidisciplinary problems.
• Excellent written and verbal communication, including experience producing research reports, technical documentation, or thought leadership content.
• Experience working in highly collaborative, cross-functional environments with research, engineering, and product stakeholders.

Who Should Apply

This role fits someone with a graduate degree in finance or a quantitative field and a track record of building evaluation frameworks or analytical methodologies. You should be comfortable translating messy financial problems into measurable research questions and working alongside ML engineers and product teams. Candidates often get rejected when they lack hands-on experience with AI evaluation or benchmarking, or when their finance expertise is broad but shallow. Another common reason for low fit is not showing how your research directly improved a model or system. If you prefer purely academic work without cross-functional collaboration, this position will not suit you.

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