Member of Technical Staff, Finance Research

🔥 13 hours ago

🗽 New York – Remote

🗽 New York – Remote

💵 $400k - $800k / year

⏰ Full Time

🔴 Lead

🖥 Software Engineer

👻 Ghost score 0%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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24-MAG

WebsiteLinkedInAll Job Openings

2 - 10 employees

🤝 B2B

💼 Consulting

B2B • Consulting

24-MAG is a commercial strategy and execution firm that helps B2B organizations design and implement systems, workflows, and operating rhythms for sales, client management, and cross-functional projects. They focus on transforming scattered processes into aligned, measurable, and scalable commercial functions—covering pipeline structure, account management frameworks, and operational discipline for teams seeking efficient, intentional growth.

📋 Description

• Design, develop, and evolve evaluation frameworks for AI systems operating across financial domains • Build benchmark suites, scoring methodologies, quality rubrics, and research-grade evaluation protocols • Conduct applied research into financial reasoning, decision-making, and workflow automation • Translate research findings into measurable improvements in AI system performance • Define methods for assessing financial intelligence across complex enterprise use cases • Develop and curate datasets, test cases, and benchmark environments for enterprise finance challenges • Translate ambiguous financial problems into measurable research questions and evaluation criteria • Design assessment scenarios for complex financial reasoning and decision-making workflows • Establish performance standards for finance-focused models and agentic systems • Support repeatable and scientifically robust evaluation processes • Analyse AI model behaviour, failure modes, reasoning patterns, and performance trends • Identify weaknesses and improvement opportunities across finance-focused models and agentic workflows • Develop methodologies for evaluating model performance across financial tasks • Produce actionable research insights guiding experimentation and research priorities • Incorporate emerging techniques from AI evaluation, machine learning, financial modelling, and agent architectures • Collaborate with AI researchers, machine learning engineers, product professionals, and research stakeholders • Produce internal research reports, technical documentation, methodological guidance, and best-practice materials • Contribute to research standards for enterprise financial intelligence and AI evaluation • Share findings with technical and finance-focused audiences • Support a distributed research environment centred on scientific rigour, experimentation, and collaborative problem-solving

🎯 Requirements

• Advanced degree in Finance, Economics, Financial Engineering, Quantitative Finance, or a closely related discipline • PhD strongly preferred • MBA, CFA, or equivalent advanced professional expertise may also be considered • Deep domain expertise in investment research, capital markets, risk management, corporate finance, accounting, or financial analysis • Demonstrated experience conducting rigorous research, developing analytical methodologies, or building evaluation frameworks • Ability to convert complex or ambiguous financial problems into measurable research questions and assessment criteria • Exceptional analytical, quantitative, and critical-thinking skills • Excellent written and verbal communication skills • Experience producing research reports, technical documentation, or thought-leadership materials • Experience working across research, engineering, product, and other cross-functional teams • Proficiency with quantitative research and data-analysis tools such as Python, SQL, statistical-analysis environments, or machine-learning frameworks is advantageous • Experience evaluating or benchmarking large language models, AI agents, reasoning systems, or enterprise AI applications is advantageous • Familiarity with agentic workflows, synthetic data generation, AI evaluation methodologies, or model-alignment techniques is advantageous • Publication history in academic research, industry research, or recognised technical publications is beneficial • Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party

🏖️ Benefits

• Fully remote • Full-time engagement • Remote consulting opportunity • Collaboration across research, engineering, product, and finance-focused stakeholders