AI Business Analyst

Overview

A multi-strategy, multi-manager hedge in New York is looking to appoint a AI Business Analyst.

The Operations team is responsible for the processes and infrastructure supporting the firm’s investment activity across strategies, products, and markets globally. Partnering closely with Portfolio Managers, Trading, Risk, Technology, and external counterparties (prime brokers, custodians, and fund administrators), the team manages the end-to-end trade lifecycle, including trade capture and settlement, position and cash reconciliations, corporate actions, and regulatory reporting.

The firm’s Operations function spans Trade Support, Reconciliations, Settlements, Confirmations, Corporate Actions, and Regulatory & Management Reporting across the trade lifecycle. The Business Analyst sits within Operations and serves as the primary conduit between Operations and Technology, translating business needs into clear requirements that drive platform development and enhancement.

This role pairs deep domain expertise and structured analysis with a hands-on, build-first mindset. Beyond owning requirements, the successful candidate identifies manual processes that can be automated or accelerated with AI, then designs, builds, and maintains lightweight solutions directly on the desk — relieving pressure on the team ahead of any broader engineering effort.

Key Responsibilities

• Partner with Operations, Trade Support, and Technology teams to understand business needs and produce comprehensive, well-structured requirements documentation.
• Translate operational workflows and business logic into clear functional and technical specifications for Technology teams to execute against.
• Conduct in-depth data analysis across asset classes, products, markets, and trading strategies to surface insights, identify process gaps, and support platform implementations.
• Engage Technology teams throughout the development lifecycle to validate that solutions meet business requirements, participating in design reviews, UAT, and production rollout.
• Identify manual, repetitive, or error-prone processes across the trade lifecycle — reconciliations, settlements, confirmations, corporate actions, and reporting — that can be automated or accelerated, and prioritize them by operational impact and risk.
• Design, build, and maintain lightweight automations and scripts (Python, SQL), including LLM-assisted workflows using tools such as Claude and ChatGPT, that connect internal data and systems to reduce manual effort on the desk.
• Prototype, pilot, and iterate on AI-assisted tools end to end, applying prompt design, testing, and evaluation to improve output quality and reliability.
• Operate and support these desk-built solutions as interim production tools — monitoring performance, troubleshooting failures, and applying appropriate controls so they remain reliable for extended use ahead of any formal engineering hand-off.
• Document each solution end to end so that successful tools can be handed to Technology for productionization, or maintained and scaled by the team in the interim.
• Proactively identify opportunities to improve Operations processes, data quality, and analytical capabilities.
• Measure the impact of automation and AI implementations against operational KPIs, identify risks introduced by automation, and recommend appropriate controls.
• Develop and maintain documentation including business requirements, process flows, playbooks, and user guides.
• Coordinate across Operations, Technology, and business stakeholders to ensure cross-functional alignment on priorities and deliverables.
• Communicate effectively at all levels — providing detailed analysis to development teams and clear, concise updates to senior stakeholders.
• Stay current on new AI capabilities and vendor releases relevant to operations use cases.

Skills, Knowledge & Experience

• 1–5 years of hands-on business analysis or operations experience in financial services, ideally within fund operations, trade support, or a related function at an investment bank, hedge fund, or buy-side firm.
• Demonstrated experience producing high-quality requirements documentation and working directly with Technology teams through the SDLC.
• Hands-on experience using LLMs such as Claude and/or ChatGPT to get real work done — prompt design, workflow automation, or similar — not just casual use.
• Working knowledge of the trade lifecycle across Equity, Fixed Income, Futures, OTC Derivatives, and FX, including reconciliations, settlements, confirmations, and corporate actions.
• Familiarity with fund operations processes and an understanding of the operational risks introduced by manual workflows.
• Proficiency in SQL — comfortable querying enterprise databases independently to interrogate large datasets and answer business questions.
• Working knowledge of Python for data analysis and process automation, with the ability to take a rough business requirement to a working, reliable tool.
• Exposure to LLM APIs, automation frameworks, or data pipeline tooling is strongly preferred.
• Strong written and verbal communication skills, with the ability to tailor messaging to technical teams and senior stakeholders alike.
• Highly organized and detail-oriented, with a proven ability to manage competing priorities independently.
• Comfortable operating in a dynamic, fast-paced environment with a proactive, self-starter mindset.
• Excellent written and verbal communication skills.
• High level of intellectual curiosity, strong work ethic, and keen attention to detail.
• Ability to work effectively in a team-oriented, fast-paced, and dynamic environment.