Key Areas of Responsibilities

• Partner with Research management, analysts, and product owners to capture AI use-case requirements and help shape the roadmap for AI-enabled Research systems.

• Lead structured analysis of Research workflows, content pipelines, and user interaction patterns toidentifywhere AI can deliver measurable productivity and quality uplift.

• Translate business needs into clear, testable requirements — process flows, functional specifications, user stories, prompt/response specs, evaluation rubrics, and acceptance criteria — that Engineering canimplementand QA canvalidate

• 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.

• Conduct hands-on data exploration, profiling, and quality analysis against research content, market data, and metadata tovalidateAI feasibility and surface data gaps.

• Support rapid prototyping and proof-of-concept validation — using notebooks, low-code tooling, and scripting — to test hypotheses,demonstratefeasibility, and de-risk requirements before formal engineering build.

• 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.

• Manage dependencies across Infra, EUS, Data, and AI engineering squads; escalate risks/issues andmaintaindelivery discipline through planning and implementation.

• Partner with Compliance and Risk to ensure regulatoryrequirements,model governance, data residency, and control requirements are embedded into AI workflows and system changes.

Requirements

• Bachelor's degree or higher in a relevant field (Computer Science, Engineering, Data Science, Quantitative Finance, or related).

• Around 5 - 7 years of experience as a Technical / Systems Business Analyst, delivering data-intensive or AI/ML-enabled solutions in complex business environments.

• Strong capability toanalysesystembehaviour, data models, ontologies, knowledge graphs, interfaces/integrations, and edge cases, and communicate effectively with engineers, data scientists, and business stakeholders.

• 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.

• 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.

• Proven ability to translate business requirements into user stories, technical specifications, and evaluation plans with clear acceptance criteria.

• Experience supporting delivery activities such asscopecoordination, dependency management, risk management, and UAT/sign-off across multi-teamprogrammes

• Self-motivated, intellectually curious, and able to thrive in a fast-paced and dynamic environment.

• Strong communciation skills in English and Chinese

• Familiarity with governance, regulatory, and compliance requirements — including data residency, model risk, and audit trails — is an advantage.

• Exposure to the financial services / research / asset management domain is a strong plus.