Data Analyst, Assistant Vice President

Responsibilities

• Data Science & Advanced Analytics
• Frame business problems into clear, testable analytical or machine-learning use cases
• Perform exploratory data analysis to identify patterns, drivers, and opportunities
• Develop, validate, and evaluate statistical, machine learning, or AI models
• Apply appropriate techniques across areas such as predictive modelling, anomaly detection, segmentation, and optimisation
• Model Development & Deployment
• Build production-quality models using modern data science and ML frameworks
• Work with engineering and platform teams to deploy models into controlled environments
• Ensure model pipelines are robust, scalable, monitored, and well-documented
• Maintain and enhance models post-deployment, including performance monitoring and retraining strategies
• Stakeholder Engagement & Product Delivery
• Partner closely with business users to understand priorities and success metrics
• Translate analytical findings into clear, actionable insights and recommendations
• Present results, trade-offs, and limitations to technical and non-technical audiences
• Contribute to roadmaps and prioritisation of data science initiatives
• Best Practices & Continuous Improvement
• Uphold standards for data quality, reproducibility, and responsible AI usage
• Contribute to reusable assets, templates, and common analytics patterns
• Mentor junior data scientists and contribute to capability building within the team
• Stay current with emerging techniques, tools, and industry best practices
• Work Experience & Technical Skills
• Typically 5-8+ years of experience in data science, analytics, or applied machine learning roles
• Strong proficiency in Python and SQL; experience with modern data science libraries and frameworks
• Solid understanding of statistics, machine learning, and model evaluation techniques
• Experience deploying models into production environments (cloud or on‑prem)
• Familiarity with data visualisation and reporting tools is an advantage
• Exposure to cloud platforms and MLOps practices is preferred but not mandatory
• Soft Skills & Competencies
• Strong problem-structuring and analytical thinking skills
• Comfortable working end‑to‑end, from ambiguity to delivery
• Able to communicate complex ideas clearly to diverse stakeholders
• Collaborative team player who works effectively across business, IT, and risk functions
• Demonstrates ownership, intellectual curiosity, and a continuous-improvement mindset
• Comfortable operating in regulated or governance-conscious environments

Additional Information

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! 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 programs 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 Senior Data Scientist will sit within the Data Hub and work closely with business, technology, and risk stakeholders to design, build, and deploy advanced analytics and AI-driven solutions. The role contributes to the full data science lifecycle, from problem framing and exploratory analysis through to model development, deployment, and ongoing performance monitoring. This role does not carry formal sign-off authority, but is accountable for technical quality, delivery outcomes, and providing well-founded recommendations to product owners, business stakeholders, and relevant governance forums.

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