Role Purpose: As a Data Scientist in the AI Lab, you will join a dynamic team focused on shaping the future of banking through AI, machine learning, and hyper-personalisation. This growth-focused role involves working on real-world problems across multiple business domains to enhance customer experience, improve operational efficiency, and support strategic decision-making. Responsibilities: Design and deploy AI-driven solutions including genAI copilots, recommendation systems, and RAG applications. Build and scale predictive and generative models using modern machine learning techniques. Explore and apply Agentic AI concepts to automate decision-making and improve customer engagement. Work with large-scale structured and unstructured data. Develop data stories through advanced visualization and storytelling. Partner with cross-functional teams to co-create solutions using cloud, MLOps, and real-time data platforms. Qualifications: A degree in Computer Science, Data Science, Statistics, or a related quantitative field. 2–5 years of hands-on experience in data science. Proficiency in Python and modern ML libraries such as TensorFlow, PyTorch, and scikit-learn, with familiarity in SQL. Experience with deep learning, NLP, or reinforcement learning, and exposure to big data technologies like Hadoop or Apache Spark. Strong communication and stakeholder engagement skills. A curious mindset with a passion for continuous learning. Benefits: Competitive base salary, holistic flexible benefits, community initiatives, and industry-leading professional development opportunities.
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A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
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01
Understand the role
What matters most
Deployment of Advanced AI Solutions
The role requires designing and deploying production-ready AI applications, specifically focusing on GenAI copilots, RAG architectures, and recommendation systems. Success depends on moving beyond experimentation to scalable, real-world implementations.
Cross-Functional Stakeholder Engagement
You must partner with diverse business domains to co-create solutions. This requires translating complex technical findings into compelling data stories that support strategic decision-making for non-technical stakeholders.
Modern ML and MLOps Integration
Building and scaling models requires proficiency in Python-based libraries like PyTorch or TensorFlow, combined with the ability to integrate these models into cloud and MLOps pipelines for real-time data processing.
Exploration of Agentic AI
A key priority is the application of Agentic AI concepts to automate decision-making processes, requiring a proactive approach to experimenting with emerging technologies to enhance customer engagement.
Likely challenges to think through
Balancing Innovation with Banking Constraints
As a financial institution, OCBC requires high standards for reliability and security. The challenge lies in applying cutting-edge, experimental AI techniques while adhering to the rigorous operational and regulatory requirements of a large-scale bank.
Managing Large-Scale Heterogeneous Data
The role involves working with both structured and unstructured data across multiple business domains. You must be prepared to handle the technical complexity of integrating big data technologies like Hadoop or Spark with modern generative models.
Rapid Technical Evolution
The field of GenAI and Agentic AI moves quickly. The challenge is maintaining a high velocity of learning and experimentation to stay current with industry trends while simultaneously delivering on immediate, high-impact business projects.
03
Practice your answers
Technical practice
Designing RAG Applications
What to coverExplain the architecture of a Retrieval-Augmented Generation system. Discuss how you would handle document chunking, vector database selection, and the trade-offs between retrieval accuracy and latency in a banking context.
Agentic AI Implementation
What to coverDescribe your approach to designing an AI agent for automated decision-making. Address how you manage agent autonomy, tool selection, and the guardrails necessary to ensure reliability and compliance in financial services.
Scaling ML Models with Big Data
What to coverHow do you transition a model from a local environment to a production-scale system using technologies like Apache Spark or Hadoop? Discuss strategies for handling large-scale structured and unstructured data pipelines.
MLOps and Model Deployment
What to coverWhat is your process for deploying and monitoring models in production? Focus on how you ensure model performance, handle drift, and maintain version control within a cloud-based MLOps framework.
Deep Learning and NLP Techniques
What to coverCompare the use cases for different NLP architectures in banking, such as transformer-based models versus traditional methods. Explain how you would fine-tune these models for specific customer engagement tasks.
Reinforcement Learning Applications
What to coverDiscuss a scenario where reinforcement learning would be more effective than supervised learning for hyper-personalization. Explain how you would define the reward function and manage exploration versus exploitation.
Data Visualization and Storytelling
What to coverHow do you translate complex model outputs into actionable insights for non-technical stakeholders? Provide an example of how you have used visualization to influence a business decision.
SQL and Data Manipulation
What to coverDescribe a complex data extraction or transformation task you performed using SQL. Focus on how you optimized queries for performance when working with large datasets.
Behavioural practice
Cross-Functional Collaboration
What to coverDescribe a time you partnered with a non-technical team to deliver an AI solution. Explain how you managed conflicting priorities and ensured the final product met their business needs.
Handling Ambiguity in Projects
What to coverTell me about a project where the requirements were unclear or the data was messy. How did you define the scope and what steps did you take to move the project forward?
Continuous Learning and Adaptation
What to coverThe AI field evolves rapidly. Provide an example of a new technique or tool you recently learned and how you applied it to improve your work or solve a specific problem.
Communicating Technical Trade-offs
What to coverGive an example of a time you had to explain the limitations of a machine learning model to a stakeholder. How did you balance their expectations with the technical reality?
02
Sharpen your knowledge
Topics to prepare
Retrieval-Augmented Generation (RAG) Architecture
The role requires designing and deploying RAG applications. Review the conceptual workflow of RAG, including document ingestion, vector database indexing, and the integration of retrieval mechanisms with generative models to ensure factual accuracy in banking contexts.
Agentic AI Concepts
The job involves exploring Agentic AI to automate decision-making. Study the theoretical framework of autonomous agents, including planning, tool use, and memory management, to understand how they can be applied to enhance customer engagement.
Skills to revise
Python ML Frameworks (PyTorch/TensorFlow)
Practice building and scaling predictive or generative models using PyTorch or TensorFlow. Focus on implementing a small-scale model that processes unstructured data to demonstrate proficiency with modern deep learning libraries.
Data Storytelling and Visualization
Develop a project where you translate complex model outputs into actionable insights for non-technical stakeholders. Use visualization tools to create a narrative that explains how a predictive model supports strategic decision-making.
MLOps and Deployment
Familiarize yourself with the MLOps lifecycle, specifically the deployment of models into production environments. Practice versioning, monitoring, and automating the pipeline for a model to ensure it remains performant and scalable.
04
Your preparation checklist
Prepare a portfolio of generative AI and RAG projects
Develop a project walkthrough demonstrating your ability to build a RAG application or a generative AI copilot. Focus on how you handled unstructured data, selected the underlying model, and ensured the output was relevant and accurate for a specific business use case.
Demonstrate proficiency in modern ML frameworks
Prepare code snippets or a GitHub repository showcasing your experience with PyTorch or TensorFlow. Highlight a specific model you built for NLP or reinforcement learning, explaining your choice of architecture and how you optimized the model for performance.
Articulate your experience with MLOps and cloud platforms
Be ready to discuss your workflow for deploying models into production. Prepare to explain how you have used MLOps practices to manage the lifecycle of a model, including monitoring, versioning, and scaling on cloud infrastructure.
Develop a data storytelling case study
Select a past project where you translated complex technical findings into actionable business insights. Prepare to explain the visualization techniques you used and how your communication influenced stakeholder decision-making.
Formulate questions for the AI Lab team
Prepare thoughtful questions regarding the bank's current approach to Agentic AI, the specific data domains you would be working with, and how the team balances experimental research with the operational requirements of a large financial institution.
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