The Data Scientist in the AI Lab will play a pivotal role in driving business decisions and creating a competitive edge for OCBC through advanced data analytics and AI solutions. You will work with large datasets, develop predictive models, and provide actionable insights to inform strategic decision-making. Responsibilities include developing and deploying predictive models, analyzing large datasets to identify trends, collaborating with cross-functional stakeholders, and designing data visualizations. You will also be responsible for architecting, scaling, and maintaining reusable AI services and GenAI-powered applications. Requirements include a degree in Computer Science, AI, Statistics, or a related field, and at least 3 years of experience in data science or generative AI solutions development. Strong programming skills in Python and SQL are required, along with experience in machine learning algorithms and data modeling. Proficiency in TypeScript or modern web frameworks is considered a strong plus for building end-to-end AI applications. Excellent communication and collaboration skills are essential. The role offers a competitive base salary, flexible benefits, and professional development opportunities within a supportive, future-ready team environment.
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Skills for this role
PythonSQLMachine LearningGenerative AIData ModelingTypeScriptData VisualizationPredictive Modeling
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A focused preparation guide, built around this job’s responsibilities and requirements.
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Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
End-to-End AI Service Development
The role requires not just modeling, but the ability to architect, scale, and maintain reusable AI services and GenAI-powered applications. You must demonstrate the ability to move beyond experimental notebooks into production-ready, scalable infrastructure.
Strategic Stakeholder Communication
A core priority is translating complex data insights into actionable strategic decisions for the business. You must be prepared to explain technical findings to non-technical stakeholders to drive business outcomes.
Predictive Modeling and Data Analysis
You are expected to analyze large, complex datasets to identify trends and develop predictive models. Proficiency in Python, SQL, and machine learning algorithms is essential for delivering these analytical solutions.
Likely challenges to think through
Bridging Data Science and Full-Stack Engineering
The preference for TypeScript or modern web frameworks suggests a need for end-to-end application development. The challenge lies in balancing rigorous data science work with the software engineering requirements of building functional AI-powered interfaces.
Navigating Enterprise AI Integration
As part of an AI Lab in a large financial institution, you will likely face the challenge of deploying models within a highly regulated environment. You should prepare to discuss how you ensure model reliability, scalability, and security when integrating AI into existing banking services.
03
Practice your answers
Technical practice
Predictive Model Deployment Lifecycle
What to coverDescribe your process for moving a predictive model from a research environment to a production-ready service. Focus on version control, automated testing, and monitoring strategies to ensure model performance remains stable over time.
Scaling AI Services
What to coverExplain the architectural considerations when scaling AI services to handle large datasets. Discuss trade-offs between batch processing and real-time inference, and how you ensure high availability for GenAI-powered applications.
Generative AI Integration
What to coverHow do you approach the development and maintenance of GenAI-powered applications? Address challenges such as prompt engineering, managing model hallucinations, and ensuring data privacy when integrating LLMs into banking workflows.
SQL and Data Modeling for Analytics
What to coverDiscuss your approach to designing data models that support complex analytical queries. Explain how you optimize SQL performance when working with large-scale datasets to identify trends and patterns.
End-to-End Application Development
What to coverGiven the preference for TypeScript or modern web frameworks, describe how you would build an end-to-end AI application. Explain how you bridge the gap between backend machine learning models and frontend user interfaces.
Data Visualization Strategy
What to coverHow do you select the most effective visualization techniques to communicate complex model insights to non-technical stakeholders? Provide an example of how a well-designed dashboard influenced a specific business decision.
Machine Learning Algorithm Selection
What to coverWhen tasked with a new predictive modeling problem, what criteria do you use to select the appropriate machine learning algorithm? Discuss the trade-offs between model complexity, interpretability, and performance.
Behavioural practice
Cross-Functional Stakeholder Collaboration
What to coverDescribe a situation where you had to collaborate with non-technical stakeholders to define a business problem. Explain how you translated their requirements into a technical solution and ensured the final output met their needs.
Translating Data Insights to Strategy
What to coverGive an example of a time your data analysis directly influenced a strategic business decision. Detail the situation, the specific insights you uncovered, and how you communicated these findings to leadership.
Staying Current in a Rapidly Evolving Field
What to coverThe AI field changes quickly. Describe how you stay updated with the latest developments in machine learning and GenAI, and provide an example of how you applied a new technique or tool to a project.
Handling Project Ambiguity
What to coverDescribe a time when you were given a vague business objective. How did you scope the project, identify the necessary data, and define success metrics to deliver a valuable AI solution?
02
Sharpen your knowledge
Topics to prepare
Generative AI Architecture
Focus on the lifecycle of GenAI applications, specifically how to architect, scale, and maintain them as reusable services. Exercise: Design a high-level system architecture for a RAG (Retrieval-Augmented Generation) pipeline that could be deployed as a microservice within a banking environment.
Predictive Modeling Lifecycle
Understand the end-to-end process of developing and deploying predictive models to drive business decisions. Exercise: Review a past project and document the specific steps taken from data preprocessing and feature engineering to model deployment and performance monitoring.
Stakeholder Communication
The role requires translating complex data insights into actionable strategic advice for non-technical stakeholders. Exercise: Practice explaining a complex machine learning model's output or a data trend to a non-technical audience, focusing on business impact rather than technical metrics.
Skills to revise
Advanced Python and SQL
Demonstrate proficiency in data manipulation and query optimization for large datasets. Exercise: Optimize a complex SQL query involving multiple joins and window functions, and write a Python script to automate a data cleaning or feature extraction task.
TypeScript and Web Frameworks
As this is a 'strong plus' for end-to-end AI applications, focus on building simple interfaces for AI models. Exercise: Create a basic web interface using a modern framework like React or Next.js that consumes a simple Python-based API endpoint to display model predictions.
Data Visualization
Design visualizations that effectively communicate insights to inform strategic decisions. Exercise: Select a complex dataset and create a dashboard or set of charts that highlight key trends and actionable business insights, ensuring clarity for a management-level audience.
04
Your preparation checklist
Prepare a portfolio of end-to-end AI projects
Curate 2-3 projects demonstrating the full lifecycle of predictive modeling or GenAI applications. Ensure you can explain your choice of algorithms, data modeling techniques, and how you scaled the solution from development to deployment.
Refine your technical narrative for Python and SQL
Prepare specific examples of how you have used Python for complex data analysis and SQL for querying large datasets to extract actionable business insights. Be ready to discuss performance optimization in your code.
Develop a case study on stakeholder communication
Prepare a structured story about a time you translated complex technical findings into a data visualization or presentation that directly influenced a strategic business decision.
Review modern web framework integration
If you have experience with TypeScript or modern web frameworks, prepare a brief explanation of how you have used these to build user-facing interfaces for AI services, as this is a stated plus for the role.
Formulate strategic questions for the interviewers
Prepare questions regarding the AI Lab's current tech stack, the specific business domains they are prioritizing for GenAI, and how the team balances experimental research with production-level service maintenance.
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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