Sr. Business Analyst, Research Data/AI, IT

CITIC CLSA; Hong Kong; On-site; HKD 500,000 - 900,000; Full time

Job summary

CITIC CLSA seeks a highly motivated Business Analyst / Project Manager to drive requirements, data analysis, and workflow design across Research IT. The role supports investment research production, content and data pipelines, publishing workflows, and supervisory functions relied upon by analysts and editors.

You will collaborate with Research management, analysts, and product owners to map processes, document business rules, and translate needs into clear requirements, user stories, and data

Qualifications

• Bachelor's degree or higher in a relevant field.
• 5–8 years delivering data-intensive solutions in financial services.
• Strong SQL for data analysis and validation; Python for profiling and prototyping.
• Experience as a Technical Business Analyst / Project Manager with multi-team delivery.
• Familiarity with Agile/Scrum, Waterfall, or hybrid methodologies.
• English and Chinese communication skills.

Responsibilities

• Analyze Research workflows and data pipelines to identify inefficiencies and automation opportunities.
• Capture requirements and shape roadmaps with stakeholders for Research systems.
• Plan end-to-end delivery, manage RAID, and coordinate cross-functional squads and vendors.
• Coordinate releases, cutover, and go-live readiness; provide executive status updates.
• Identify AI enablement opportunities within research workflows and validate concepts.

Skills

SQL

Python

Data analysis

Project management

Agile / Scrum / Hybrid

Jira

Azure DevOps

MS Project/Planner

English and Chinese

Education

Bachelor's degree or higher

Tools

Jira

Azure DevOps

MS Project/Planner

Job description

Position DescriptionSeeking 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 ResponsibilitiesInvestment Research Domain & WorkflowsDevelop 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 & RequirementsConduct 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 standardsPartner 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 criteriaProject & Delivery ManagementPlan 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 escalate 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 EnablementIdentify 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 & ControlsCoordinate 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.RequirementsBachelor'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 modelsStrong working knowledge of SQL for data analysis and validation; confident with Python for data profiling, exploratory analysis, and prototypingProven 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.