Gen AI Architect for Wealth Advisory

DBS Bank

Hong Kong

On-site

HKD 1,200,000 - 1,800,000

Full time

8 days ago

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Job summary

DBS Bank Hong Kong is seeking a senior data scientist to lead the end-to-end design, development and deployment of Gen AI and agentic AI applications for the Investment Product & Advisory business, focusing on affluent clients.

You will collaborate with regional teams, translate business challenges into high-impact data deliverables, and ensure governance, risk, and operational excellence while mentoring peers in modern DS practices and MLOps.

Qualifications

Minimum 7 years of hands-on data science and advanced analytics experience within financial services, fintech, or wealth management.
Experience building and deploying Gen AI, LLM applications, or Agentic AI frameworks in production.
Strong proficiency in Python/PySpark and Cloud platforms (GCP strongly preferred).
Strong AI foundation in ML/NLP/DL and statistical modeling.
Experience with enterprise BI tools (Qlik Sense is a plus).
Working knowledge of consumer investment products and frontline advisory workflows.
Proven track record of supporting technological transformations and upskilling peers.
Strong business acumen with ability to translate AI concepts into business value.
Fluency in English and Cantonese.

Responsibilities

Lead the end-to-end design, development, and deployment of Gen AI and Agentic AI applications (e.g., Advisory Co-pilot and Automated Sales Surveillance) for Investment Product & Advisory, targeting affluent clients.
Assist the Team Lead in executing the analytics roadmap and shifting the team toward a modern data science-oriented workforce.
Partner with Product Heads and Advisory Sales Heads to translate business challenges into high-impact data deliverables.
Build, fine-tune, and evaluate LLMs and autonomous AI agent workflows for wealth management scenarios.
Develop ML solutions to enhance customer targeting, cross-selling, and investment growth.
Work with Regional teams to implement and scale enterprise AI solutions.
Ensure AI models, agentic workflows, and data pipelines comply with governance and risk frameworks.
Establish evaluation frameworks and monitoring metrics for performance, safety, guardrails, and impact.
Champion MLOps and mentor team members in ML/AI modeling.