Sr. Business Analyst, Research Data/AI, IT

CLSA; Hong Kong; On-site; HKD 700,000 - 1,100,000; Full time

Job summary

CLSA is seeking a highly motivated Business Analyst / Project Manager to drive high-quality requirements, data analysis, workflow design, and delivery execution across Research IT. The role spans research production, content and data pipelines, publishing/distribution workflows, market data, and systems relied on by analysts and supervisors.

The ideal candidate will have 5–8 years in technical BA/PM roles within financial services or asset management, strong SQL/Python skills, and experience

Qualifications

• Bachelor's degree or higher in a relevant field.
• 5–8 years in Technical Business Analysis / Project Management in financial services or asset management preferred.
• Strong SQL for data analysis; Python for profiling and prototyping.
• Proven experience delivering data-intensive solutions across multi-team initiatives.

Responsibilities

• Analyze research workflows and data pipelines to identify bottlenecks and automation opportunities.
• Capture requirements, write user stories, data specs, and acceptance criteria for delivery.
• Plan end-to-end delivery across squads and multiple vendors; manage risks and timelines.
• Coordinate engineering, data, UI/UX, and TechOps teams; run stand-ups and remove blockers.
• Provide updates to senior stakeholders and support governance and control processes.
• Explore AI enablement opportunities and evaluate proof-of-concept outcomes.

Skills

SQL

Python

Communication

Project management

Data analysis

Agile/Scrum

Stakeholder management

Education

Bachelor's degree or higher

Tools

Jira

Azure DevOps

MS Project/Planner

Job description

Seeking a highly motivated and technically capable Business Analyst / Project Manager to drive high-quality requirements, data analysis, workflow design, and delivery execution across Research IT. The role sits at the heart of the investment research business — spanning research production, content and data pipelines, publishing and distribution workflows, market and reference data, and the systems that analysts, editors, and supervisory functions rely on every day.

Key Areas of Responsibilities

Investment Research Domain & Workflows

• Develop deep domain knowledge of the research lifecycle — idea generation, authoring, editing, supervisory/compliance review, publishing, entitlements, and distribution to clients and internal consumers.
• Conduct structured analysis of Research workflows, content pipelines, and user interaction patterns to identify inefficiencies, manual bottlenecks, and opportunities for automation and quality uplift.
• Partner with Research management, analysts, and product owners to capture requirements and help shape the roadmap for Research systems.
• Map current-state and target-state processes; document business rules, edge cases, and hand-offs across the research value chain.

Data Analysis & Requirements

• Conduct hands-on data exploration, profiling, and quality analysis across research content, market data, reference/entity data, and metadata to validate feasibility and surface data gaps.
• Define and maintain data requirements, data dictionaries, taxonomies, ontologies, and metadata standards
• Partner with Data Engineering to identify data quality issues, lineage requirements, and enrichment opportunities.
• Translate business needs into clear, testable requirements — process flows, functional specifications, user stories, data specs, and acceptance criteria

Project & Delivery Management

• Plan and manage delivery of Research IT initiatives end-to-end — defining scope, milestones, timelines, resourcing needs, and dependencies across multiple squads and vendors.
• Maintain project plans, delivery roadmaps, RAID logs (risks, assumptions, issues, dependencies), and status reporting for stakeholders and management.
• Drive day-to-day execution — coordinating engineering, data, UI/UX, and TechOps teams, running stand-ups/working sessions, and removing blockers to keep delivery on track.
• Track budget, effort, and progress against plan; proactively elevate risks and manage change to protect scope, timeline, and quality.
• Coordinate releases, cutover, and go-live readiness, ensuring stakeholder sign-off and smooth transition to support/BAU.
• Provide clear, concise updates to senior stakeholders (including ExCo/SteerCo-level reporting) on delivery status, risks, and decisions required.

AI Enablement

• Identify where AI can deliver measurable productivity and quality improvements within research workflows — e.g. drafting support, summarisation, tagging/classification, newsfeed intelligence, and recommendations.
• Support proof-of-concept validation and evaluation of AI features, including test sets, scoring approaches, and acceptance criteria, so AI is applied pragmatically and safely.
• Help define prompt/response expectations, guardrails, and human-in-the-loop review steps where AI is embedded into workflows.

Governance & Controls

• Coordinate UAT, human-in-the-loop reviews, and release readiness with business users and technical teams.
• Partner with Compliance and Risk to ensure regulatory requirements, data residency, and control requirements are embedded into workflows and system changes.

Requirements

• Bachelor's degree or higher in a relevant field (Computer Science, Engineering, Data Science, Quantitative Finance, etc).
• Around 5–8 years of experience as a Technical Business Analyst / Project Manager, delivering data-intensive solutions, within financial services, investment research, or asset management is preferred.
• Strong capability to analyse business workflows, system behaviour, data models
• Strong working knowledge of SQL for data analysis and validation; confident with Python for data profiling, exploratory analysis, and prototyping
• Proven project/delivery management experience — planning, scheduling, dependency and risk management, stakeholder reporting, and driving multi-team initiatives to on‑time delivery (would be considered as Project Manager)
• Familiarity with delivery methodologies (Agile/Scrum, Waterfall, or hybrid) and tooling (e.g. Jira, Azure DevOps, MS Project/Planner).
• Working understanding of modern AI concepts — LLMs, RAG, embeddings, and evaluation basics — sufficient to scope features, challenge vendor claims, and define acceptance criteria.
• Self‑motivated, intellectually curious, and able to thrive in a fast‑paced, dynamic environment.
• Strong communication skills in English and Chinese.
• Familiarity with governance, regulatory, compliance requirements and audit trails — will be of advantage.