Seeking a highly motivated and technical BA to drive high-quality requirements, model evaluation, and technical analysis across our AI initiatives in Research IT — including AI-driven research workflows, agentic assistants, newsfeed intelligence, and recommendation systems. The role is responsible for end-to-end requirements across business workflows, data, model behaviour, and system integrations, translating Research needs into clear, testable specifications and structured experiments that guide our AI delivery.

Act as the key interface between Research users, AI vendors, and internal Technology teams — Engineering, Data, UI/UX, TechOps, and Compliance to ensure our AI solutions are accurate, explainable, secure, and aligned with governance and control requirements.

Key Areas of Responsibilities

1. Partner with Research management, analysts, and product owners to capture AI use-case requirements and help shape the roadmap for AI-enabled Research systems.
2. Lead structured analysis of Research workflows, content pipelines, and user interaction patterns to identify where AI can deliver measurable productivity and quality uplift.
3. Translate business needs into clear, testable requirements — process flows, functional specifications, user stories, prompt/response specs, evaluation rubrics, and acceptance criteria — that Engineering can implement and QA can validate.
4. Define and run model evaluation and benchmarking activities: design test sets, golden datasets, scoring methodologies, and side-by-side comparisons across candidate models (e.g., GPT, Qwen, DeepSeek) and vendor solutions.
5. Conduct hands-on data exploration, profiling, and quality analysis against research content, market data, and metadata to validate AI feasibility and surface data gaps.
6. Support rapid prototyping and proof-of-concept validation — using notebooks, low-code tooling, and scripting — to test hypotheses, demonstrate feasibility, and de-risk requirements before formal engineering build.
7. Coordinate UAT, human-in-the-loop reviews, and release readiness with business users and technical teams; ensure AI outputs meet quality, latency, and explainability expectations.
8. Manage dependencies across Infra, EUS, Data, and AI engineering squads; escalate risks/issues and maintain delivery discipline through planning and implementation.
9. Partner with Compliance and Risk to ensure regulatory requirements, model governance, data residency, and control requirements are embedded into AI workflows and system changes.

Requirements

1. Bachelor's degree or higher in a relevant field (Computer Science, Engineering, Data Science, Quantitative Finance, or related).
2. Around 5 - 7 years of experience as a Technical / Systems Business Analyst, delivering data-intensive or AI/ML-enabled solutions in complex business environments.
3. Strong capability to analyse system behaviour, data models, ontologies, knowledge graphs, interfaces/integrations, and edge cases, and communicate effectively with engineers, data scientists, and business stakeholders.
4. Strong working knowledge of SQL for data analysis and validation; comfortable using Python (or similar) for data profiling, exploratory analysis, and prototyping — not as a primary engineering function, but to support rigorous requirements and evaluation work.
5. Practical understanding of modern AI concepts — LLMs, RAG, embeddings, vector search, agent orchestration, prompt design, and evaluation frameworks — sufficient to scope features, challenge vendor claims, and define acceptance criteria.
6. Proven ability to translate business requirements into user stories, technical specifications, and evaluation plans with clear acceptance criteria.
7. Experience supporting delivery activities such as scope coordination, dependency management, risk management, and UAT/sign-off across multi-team programmes.
8. Self-motivated, intellectually curious, and able to thrive in a fast-paced and dynamic environment.
9. Strong communciation skills in English and Chinese
10. Familiarity with governance, regulatory, and compliance requirements — including data residency, model risk, and audit trails — is an advantage.
11. Exposure to the financial services / research / asset management domain is a strong plus.