AI-Driven Risk Analytics Lead for TPRM

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

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.