Bank of Singapore opens doors to new opportunities.

At Bank of Singapore, we are constantly on the lookout for exceptional individuals to join our team. We promote a culture of openness, teamwork and fairness. Most importantly, we invest in our people through our programmes that develop them on both professional and personal levels. Besides attractive remuneration packages, we offer non-financial benefits and opportunities to develop your potential within OCBC Group’s global network of subsidiaries and offices. If you have passion, drive and the will to succeed, rise to the challenge today!

The Team Lead – Data Science & AI Engineering will be part of the COO Data Hub and will play a key role in supporting strategic initiatives in building and maintaining AI/ML applications.

With a strategic cloud platform being onboarded , the Data Hub is expected to:

• Support production deployment and ongoing maintenance of AI solutions

• Validate and govern AI/ML models

• Provide implementation support, code reviews, and best practices

• Enable multiple business use cases leveraging AI and advanced analytics capabilities available on the cloud platform

This role is designed to close critical capability gaps by providing technical leadership, hands‑on expertise, and delivery ownership across AI/ML solutions running in a regulated, production‑grade environment.

Key Responsibilities

Technical & Delivery Leadership

• Act as team lead for a small group of data scientists and AI / software engineers

• Provide technical direction, coaching, and mentoring to ensure high engineering and data science standards

• Own solution design decisions and review architecture, code, and model implementations

• Promote best practices in model development, MLOps, software engineering, and documentation

Productionisation & MLOps

• Drive the transition of POCs and experimental models into production

• Design and implement MLOps pipelines covering:

  • CI/CD for models and data pipelines

  • Model deployment, versioning, and rollback

  • Monitoring for data drift, model performance, and operational health

• Ensure solutions are reliable, maintainable, and cost‑efficient in production

AI / ML Solution Development

• Lead end‑to‑end delivery of AI and ML use cases, including:

  • Data exploration and feature engineering

  • Model development, training, evaluation, and validation

  • Model performance monitoring and retraining strategies

• Support model validation activities in line with internal model risk management and governance requirements

Cloud Engineering

• Design and operate AI and data workloads on Cloud Platform

• Leverage Cloud services such as (but not limited to):

  • BigQuery, Cloud Storage

  • Vertex AI and related ML services

  • GKE / container‑based workloads

• Work closely with cloud and platform teams to align with security, IAM, networking, and operational standards

Stakeholder & Cross‑Team Collaboration

• Act as a bridge between business, data science, engineering, cloud, and governance teams

• Translate business problems into well‑defined technical solutions and delivery plans

• Provide implementation guidance and technical input to multiple teams seeking to leverage GCP AI capabilities

Work Experience Requirement

• 8+ years of experience in data science, software engineering, or related roles

• Proven experience as a technical team lead or hands‑on manager for small teams

• Strong track record of moving AI / data solutions from POC to production

• Practical experience operating workloads in cloud environments, preferably Google Cloud Platform

• Experience working in regulated or risk‑sensitive environments is a strong advantage

• Strong programming skills in languages such as Python, JS

• Solid understanding of:

  • Data pipelines and analytics workflows

  • Software engineering best practices

• Hands‑on experience with following is preferred:

  • GCP services (BigQuery, GKE, Vertex AI, IAM, monitoring)

  • Containerisation (Docker) and orchestration (Kubernetes)

  • CI/CD pipelines and Infrastructure as Code (e.g. Terraform)

• Familiarity with MLOps concepts and tooling

Soft Skills & Competencies

• Strong leadership and mentoring skills, with the ability to guide and develop team members

• Excellent communication skills, able to explain complex technical concepts to non‑technical stakeholders

• Comfortable operating in ambiguity and driving initiatives with partial information

• Structured problem‑solver with a data‑driven and pragmatic mindset

• Autonomous, curious, and proactive, with a strong sense of ownership