Business Analyst - AI & Advanced Risk Analytics

Randstad Hong Kong Limited; Hong Kong; On-site; HKD 900,000 - 1,400,000; Full time

Job summary

AIA Group is seeking a data science professional within its Group Operational Risk function to design, develop, and implement analytics, ML and AI/LLM-driven solutions that automate risk scoring, continuous vendor monitoring, anomaly detection, and risk assessments across its operating units.

The role spans requirements architecture, AI/LLM prototyping, predictive modeling, cloud data pipelines, and governance documentation in line with enterprise AI governance guidelines.

Qualifications

• Degree in CS/DS/Statistics or related field.
• Proficient in Python and ML libraries.
• Hands-on with LLMs and LangChain.
• Experience with cloud platforms (AWS/Azure/GCP).
• Strong SQL and handling large datasets.
• Experience in Banking/Insurance risk is a plus.
• Excellent communication to non-technical stakeholders.
• Fluent English; Cantonese or Mandarin advantageous.

Responsibilities

• Design, develop and deploy ML/AI-driven risk scoring and vendor monitoring solutions.
• Translate business challenges into data science requirements with risk teams.
• Prototype and integrate Generative AI/LLMs for contract reviews and risk workflows.
• Build predictive models to detect operational risk and anomalies.
• Create data pipelines and risk data marts in secure cloud environments.
• Produce documentation: specs, data dictionaries, user stories, governance.

Skills

Python

Pandas

NumPy

Scikit-learn

PyTorch

TensorFlow

LangChain

RAG architectures

API integrations

SQL

AWS

Azure

GCP

Communication

Education

Degree in Computer Science, Data Science, Statistics, Information Systems, Financial Engineering, or related quantitative discipline

Tools

AWS

Azure

GCP

Job description

about the company.

Role Summary

Sitting within the wider Group Operational Risk function and supporting the Third Party & Outsourcing Risk team, this role bridges business risk requirements with modern technology. You will design, develop, and implement advanced analytics, Machine Learning (ML), and AI/LLM-driven solutions to automate risk scoring, continuous vendor monitoring, anomaly detection, and operational risk assessments across AIA Group's operating units.

• Requirements & Solution Architecture: Collaborate with Group Risk leaders to elicit, evaluate, and translate business challenges into technical data science and AI-driven solutions.
• AI & LLM Implementation: Assist in prototyping and integrating Generative AI / Large Language Models (LLMs) to streamline contract review, vendor risk tiering, policy compliance checks, and regulatory assessment workflows.
• Predictive Modeling & Analytics: Develop and maintain Machine Learning and statistical models using Python to identify emerging third-party operational vulnerabilities, concentration risks, and performance anomalies.
• Cloud & Data Pipelines: Work alongside enterprise architecture and IT to build and optimize data ingestion pipelines and risk data marts across secure cloud environments.
• Documentation & Governance: Produce functional specifications, data dictionaries, user stories, and model governance documentation in compliance with enterprise AI and model risk management guidelines.

Key Requirements

• Education: Degree in Computer Science, Data Science, Statistics, Information Systems, Financial Engineering, or a related quantitative discipline.
• Technical Proficiency:
• Proficient in Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow, etc.).
• Practical understanding of LLMs / GenAI frameworks (e.g., prompt engineering, RAG architectures, LangChain, API integrations).
• Hands-on experience with modern Cloud platforms (AWS, Azure, or GCP).
• Strong SQL skills and experience manipulating large-scale, unstructured datasets.
• Functional Domain: Prior exposure to Operational Risk, Non-Financial Risk (NFR), Third-Party Risk Management (TPRM), or Compliance within Banking, Insurance, or Financial Services is preferred.
• Communication: Ability to articulate complex data science and AI concepts clearly to non-technical business stakeholders.
• Language: Fluent in English (written and verbal); Cantonese or Mandarin is an advantage.