Technical Business Analyst - AI Transformation

Randstad Hong Kong Limited; Hong Kong; Vor Ort; HKD 900.000 - 1.700.000; Vollzeit

Zusammenfassung

Randstad Hong Kong Limited is seeking a Technical Business Analyst - AI Transformation to join the Regional Risk Management transformation team in Hong Kong. You will bridge Risk leadership, Data Science, and Cloud Engineering to deliver AI/GenAI risk initiatives.

You will lead end-to-end AI/LLM risk projects, assess feasibility and costs, run discovery workshops, manage backlogs, and craft granular user stories for sprints, while ensuring governance, data readiness, and regulatory

Qualifikationen

• 8+ years of experience as a Business Analyst, Project Manager, or AI Product Manager in Financial Services, Insurance, or Risk Management.
• Demonstrated Agile project management, backlog prioritization, user story creation, and cross-functional sprint delivery.
• Hands-on proficiency in Python and SQL for data exploration, prototyping, and analysis.
• Solid understanding of Machine Learning, LLMs/GenAI, and data science lifecycles.

Aufgaben

• Drive end-to-end delivery of AI/LLM risk projects, bridging Risk leadership, Data Science, and Cloud Engineering.
• Evaluate AI/LLM use cases for feasibility, costs, ROI, and resources.
• Lead discovery workshops to identify optimization opportunities using GenAI and ML.
• Manage product backlogs, map user journeys, and write granular technical user stories with acceptance criteria.
• Oversee PoC activities, validate model performance, and prepare executive reviews.

Kenntnisse

Python

SQL

Agile project management

Backlog prioritization

User story creation

Stakeholder management

Ausbildung

Bachelor's or Master's degree in Computer Science / Data Science / Quantitative Finance

Tools

AWS

Azure

GCP

Jobbeschreibung

Technical Business Analyst - AI Transformation

Our client is a premier global life insurance and financial services group in Hong Kong with a major Asia-Pacific footprint. They invest heavily in digital transformation, cloud migrations, and cutting-edge artificial intelligence, building scalable, compliant, and data-driven risk management infrastructure to support regional growth and enterprise security.

about the team.

You will join the Regional Risk Management transformation team, working at the intersection of risk policy, data engineering, and quantitative science. The team culture is highly technical, collaborative, and delivery-focused, offering an agile platform to prototype GenAI use cases, validate system architectures, and deploy advanced risk models.

about the job.

Drive end-to-end delivery of AI/LLM risk projects, acting as the primary bridge between Risk business leadership, Data Science, and Cloud Engineering units.

Evaluate incoming AI/LLM use cases across Risk domains, assessing technical feasibility, compute costs, ROI, and resource allocation.

Lead discovery workshops to identify opportunities for process optimisation, automated risk monitoring, and workflow enhancement using GenAI and Machine Learning.

Manage product backlogs, map user journeys, and write granular technical user stories with explicit acceptance criteria for engineering sprints.

Oversee Proof-of-Concept (PoC) activities, validate model performance metrics, and consolidate assessment results for executive steering committees.

Formulate business cases, define benefits realisation frameworks, and track KPIs to evaluate commercial impact.

Partner with AI and data engineering teams to run prototype data queries in Python and SQL, ensuring data readiness and pipeline integrity.

Document regulatory considerations, governance requirements, and audit trails necessary for enterprise AI adoption in risk platforms.

Support project migration, testing, and deployment of risk analytics models across Cloud Platforms (AWS, Azure, or GCP).

skills & experience required.

Minimum 8+ years of experience as a Business Analyst, Project Manager, or AI Product Manager within Financial Services, Insurance, or Risk Management.

Demonstrated track record in Agile project management, backlog prioritization, user story creation, and cross-functional sprint delivery.

Hands-on proficiency in Python and SQL for data exploration, prototyping, and analysis.

Solid conceptual and practical understanding of Machine Learning (ML), LLMs / GenAI, and Data Science lifecycles.

Practical understanding of Cloud Computing architectures (AWS, Azure, or GCP).

Proven experience conducting PoC evaluations, process mapping, and executive stakeholder management.

Bachelor's or Master's degree in Computer Science, Quantitative Finance, Data Science, or a related quantitative field.

Excellent communication and stakeholder management skills in Cantonese and English are required.