New opportunity

MGR/AVP, AI and Data Analyst

OCBC Bank · Singapore

About this role

As an AI & Data Analyst in the Group Risk Management (GRM) transformation team, you will leverage AI and data to develop practical use cases that support business growth, improve customer engagement, enhance operational efficiency, and strengthen decision-making. You will act as a bridge between business stakeholders, technology teams, and data scientists to translate business challenges into implementable AI, digital, and data solutions. Responsibilities include promoting an AI-enabled culture, building solutions using traditional AI, machine learning, GenAI, and Agentic AI, and providing oversight on model selection and cost estimation for GenAI workloads. You will also ensure adherence to data governance, privacy, and responsible AI requirements. Required qualifications include a degree in Analytics, Statistics, Mathematics, Computer Science, Information Systems, Business, Economics, or a related field, and a minimum of 5 years of experience in AI, data analytics, or business intelligence. Candidates must possess strong analytical, communication, and stakeholder management skills. Experience in banking or regulated industries is considered advantageous. The role offers a competitive base salary and flexible benefits.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Large Language Models (LLMs)Retrieval-Augmented Generation (RAG)AI application developmentAgentic AIPythonSQLPower BIDevOpsConfluentBitbucketJenkinsJIRAAmazon SageMakerAmazon BedrockAmazon QuickSightAthenaRedshiftMicrosoft CopilotMicrosoft Power AutomateData analysisFinancial risk concepts

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Bridging Technical and Business Domains
The role requires acting as a translator between non-technical business stakeholders, risk managers, and technical teams (data scientists/engineers). You must demonstrate the ability to convert complex business challenges into actionable AI, digital, and data solutions while ensuring alignment with organizational goals.
Responsible AI and Governance Oversight
You are responsible for ensuring all AI and analytics solutions adhere to strict data governance, privacy, and regulatory requirements. This includes providing oversight on model selection and ensuring that AI deployment remains compliant with the bank's responsible AI framework.
GenAI Workload Management and Benefit Realization
You must guide departments on the practical application of GenAI and Agentic AI. This involves not only building solutions but also managing the economic aspect, specifically providing oversight on cost estimation for GenAI workloads and measuring the tangible business benefits of these implementations.

Likely challenges to think through

Navigating Regulatory Constraints in AI Adoption
Implementing cutting-edge technologies like Agentic AI in a highly regulated banking environment creates a tension between innovation and compliance. Prepare by articulating how you balance rapid prototyping with the rigorous documentation and risk-mitigation standards required in financial services.
Managing Diverse Stakeholder Expectations
You will work with various departments, each with different risk appetites and technical literacy levels. The challenge is to maintain a consistent AI-enabled culture while managing conflicting priorities. Prepare by sharing examples of how you have influenced stakeholders through evidence-based recommendations and clear communication.
Demonstrating ROI for Emerging Technologies
GenAI and Agentic AI projects can be costly and difficult to quantify in terms of immediate financial impact. You will be expected to provide oversight on cost estimation and benefit realization. Prepare by discussing how you define success metrics for AI projects and how you justify the resource allocation for experimental technologies.

Practice your answers

Technical practice

Implementing Retrieval-Augmented Generation (RAG) in Banking

What to coverExplain how you would design a RAG pipeline to ensure data accuracy and security when querying sensitive financial documents. Discuss the trade-offs between vector database selection and the importance of metadata filtering in a regulated environment.

Cost Estimation and Model Selection for GenAI Workloads

What to coverHow do you approach selecting between open-source models and proprietary APIs when considering both performance and long-term operational costs? Suggest a framework for estimating token usage and infrastructure expenses for a new GenAI use case.

Agentic AI vs. Traditional Automation

What to coverDescribe a scenario where an Agentic AI approach would be superior to traditional rule-based automation in a risk management context. Focus on how you would manage the autonomy of agents while maintaining strict governance and human oversight.

Data Governance and Responsible AI

What to coverWhat specific steps do you take to ensure that AI models deployed in a bank comply with data privacy regulations and internal ethical standards? Address how you handle model bias, explainability, and the documentation required for auditability.

Bridging Technical and Business Teams

What to coverDescribe your process for translating a vague business requirement from a risk manager into a technical specification for data scientists. How do you ensure that the final deliverable aligns with the original business objective?

Leveraging AWS for AI/ML Pipelines

What to coverGiven the stack includes Amazon SageMaker, Bedrock, and Redshift, how would you architect an end-to-end pipeline that moves data from a warehouse to a model inference endpoint? Discuss the role of DevOps tools like Jenkins or Bitbucket in this lifecycle.

Performance Monitoring of Deployed AI Solutions

What to coverOnce an AI solution is in production, what metrics do you track to measure its effectiveness and business impact? Explain how you would identify and mitigate model drift in a financial risk application.

SQL and Python for Risk Analysis

What to coverHow do you utilize Python and SQL to perform exploratory data analysis on complex, high-volume financial datasets? Provide an example of how you have used these tools to uncover actionable insights for a business stakeholder.

Behavioural practice

Managing Conflicting Stakeholder Priorities

What to coverDescribe a time when you had to balance the competing needs of business stakeholders, data scientists, and risk compliance teams. How did you negotiate a solution that satisfied all parties while keeping the project on track?

Promoting an AI-Enabled Culture

What to coverGive an example of how you have advocated for the adoption of new AI technologies within a team that was previously resistant to change. What strategies did you use to demonstrate the value and build confidence?

Handling Project Setbacks

What to coverTell me about a complex AI or data project that did not go as planned. What was your role in identifying the issue, and how did you communicate the impact and potential pivot to your stakeholders?

Ensuring Accountability in Complex Deliverables

What to coverProvide an example of how you managed the end-to-end execution of a high-stakes project. How did you track progress, manage timelines, and ensure that all regulatory and governance requirements were met throughout the process?

Sharpen your knowledge

Topics to prepare

Responsible AI and Governance in Banking
Review frameworks for AI ethics, data privacy, and regulatory compliance within the financial sector. Exercise: Draft a checklist for evaluating a new AI use case against common banking data governance and privacy standards to ensure responsible deployment.
GenAI Workload Economics and Model Selection
Understand the cost drivers of GenAI, including token usage, infrastructure, and maintenance. Exercise: Create a hypothetical cost-benefit analysis template for a RAG-based application, comparing different model selection strategies and their impact on operational efficiency.
Agentic AI and Workflow Automation
Study the conceptual shift from simple LLM chatbots to autonomous agents capable of multi-step task execution. Exercise: Map out a business process in risk management and identify where an agentic workflow could replace manual steps to improve productivity.

Skills to revise

RAG and LLM Application Development
Practice building or refining RAG pipelines to improve retrieval accuracy and reduce hallucinations. Exercise: Build a small-scale prototype using Python that retrieves information from a specific document set to answer domain-specific questions, focusing on data source integration.
Stakeholder Translation and Requirements Gathering
Develop the ability to bridge technical AI capabilities with business risk management objectives. Exercise: Practice explaining a complex technical concept like 'Agentic AI' to a non-technical stakeholder, focusing on the business value and risk mitigation rather than the underlying architecture.
Data Analysis and Visualization with SQL and Power BI
Ensure proficiency in querying complex datasets and creating actionable dashboards. Exercise: Use SQL to extract a risk-related dataset and design a Power BI dashboard that highlights key performance indicators relevant to operational efficiency in a banking context.

Your preparation checklist

Prepare a project walkthrough on AI-driven risk solutions
Develop a concise presentation detailing a past project where you bridged the gap between business requirements and technical implementation. Highlight your use of Python, SQL, or LLMs to solve a specific business problem, emphasizing how you managed stakeholders and ensured data governance.
Articulate your experience with GenAI and RAG frameworks
Prepare to discuss your hands-on experience with Retrieval-Augmented Generation (RAG) and LLMs. Be ready to explain how you select models, estimate costs for GenAI workloads, and ensure the responsible use of AI in a regulated environment.
Review financial risk concepts and regulatory requirements
Refresh your knowledge of financial or non-financial risk concepts relevant to banking. Prepare examples of how you have navigated data privacy and regulatory compliance in previous analytics projects to demonstrate your commitment to responsible AI.
Draft questions regarding the GRM transformation team
Prepare thoughtful questions for the interviewer about the current AI initiatives within Group Risk Management, the primary challenges in bridging business and technical teams, and how the team measures the benefit realization of new AI deployments.
Demonstrate proficiency with the technical stack
Be prepared to discuss your experience with the specific tools mentioned in the job description, such as AWS services (SageMaker, Bedrock, Redshift), DevOps tools (Jenkins, JIRA, Bitbucket), and visualization tools like Power BI. Focus on how these tools supported your end-to-end project delivery.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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