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Open nowFirst seen 6 days ago

Member of Technical Staff, Finance Research

ESRhealthcare and EXEC STAFF RECRUITERS1,405 open roles

Where
NYC, NY, USA, Remote
Work mode
Remote
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Your applicationOpen nowMember of Technical Staff, Finance ResearchESRhealthcare and EXEC STAFF RECRUITERS · NYC, NY, USA, Remote

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Share of postings closed within
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  5. 35.6%30 days
  6. 58.0%60 days
This job: first seen 6 days ago

The posting

Member of Technical Staff, Finance Research

Job Type: Full-time

Location: Remote

The Role

Join our research team as a Member of Technical Staff (MTS), Finance Research, where you'll help define the frontier of AI-powered financial reasoning. This role sits at the intersection of large language models, agentic systems, and enterprise finance, with a focus on building rigorous evaluation frameworks that measure, benchmark, and advance AI performance across complex financial workflows.

You will conduct applied research, develop novel methodologies for assessing financial intelligence in AI systems, and partner closely with researchers, engineers, and product teams to shape the next generation of enterprise AI.

What You'll Be Doing

Design, own, and evolve evaluation frameworks for AI agents operating in financial domains, including benchmark suites, scoring methodologies, quality rubrics, and research-grade evaluation protocols.

Conduct original research on financial reasoning, decision-making, and workflow automation, translating findings into measurable improvements in AI system performance.

Develop and curate datasets, test cases, and benchmark environments that capture real-world enterprise finance challenges.

Collaborate with AI researchers, machine learning engineers, and product teams to evaluate, validate, and improve finance-focused models and agentic systems.

Analyze model behavior, failure modes, and performance trends to generate actionable insights and guide research priorities.

Publish internal research findings, technical reports, and best practices that contribute to the broader understanding of AI capabilities in finance.

Stay current with advances in AI, machine learning, financial modeling, evaluation science, and agent architectures, incorporating emerging techniques into research initiatives.

Help establish research standards and thought leadership for enterprise financial intelligence and AI evaluation.

Foster a culture of scientific rigor, experimentation, and collaborative innovation within a distributed research environment.

What We're Looking For

Advanced degree in Finance, Economics, Financial Engineering, Quantitative Finance, or a related field (PhD strongly preferred; MBA, CFA, or equivalent expertise also considered).

Deep domain expertise in one or more areas of finance, including investment research, capital markets, risk management, corporate finance, accounting, or financial analysis.

Demonstrated experience conducting rigorous research, developing analytical methodologies, or building evaluation frameworks in finance or related quantitative disciplines.

Strong ability to translate ambiguous real-world financial problems into measurable research questions and evaluation criteria.

Exceptional analytical and critical-thinking skills, with a track record of solving complex, multidisciplinary problems.

Excellent written and verbal communication skills, including experience producing research reports, technical documentation, or thought leadership content.

Experience working in highly collaborative, cross-functional environments involving research, engineering, and product stakeholders.

Preferred Qualifications

Experience evaluating, benchmarking, or researching large language models, AI agents, reasoning systems, or enterprise AI applications.

Familiarity with agentic workflows, AI evaluation methodologies, synthetic data generation, or model alignment techniques.

Knowledge of contemporary research in machine learning, financial AI, decision intelligence, or computational finance.

Experience designing industry benchmarks, assessment frameworks, or performance standards.

Publication record in academic conferences, journals, industry research, or influential technical publications.

Proficiency with data analysis, experimentation, or quantitative research tools (e.g., Python, SQL, statistical analysis, or machine learning frameworks).

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