VP - Data Analytics (ML, Python,Big Data)
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Summary
VP-level data analytics role at a leading bank in Singapore, driving end-to-end delivery of enterprise data, analytics, and AI/ML-enabled solutions with a focus on regulatory compliance, data governance, and customer insights. Core tech includes modern data platforms (Databricks, Snowflake, BigQuery, Hadoop/Spark), SQL, Python, and BI tools.
Join a leading bank as a VP - Data Analytics, where you will drive the delivery of enterprise data, analytics, and AI-enabled solutions that support business growth, regulatory compliance, risk management, and customer insights across the organization.
Lead end-to-end delivery of enterprise data, analytics, and AI-enabled solutions across the full SDLC, from requirements gathering through implementation and production support.
Analyze business and regulatory requirements and translate them into scalable data architectures, functional designs, and solution roadmaps
Design and govern modern data platforms supporting structured, semi-structured, and unstructured data, including advanced analytics, machine learning, NLP, and real-time insights.
Collaborate with business, technology, data science, and operations teams to ensure solution quality, data integrity, regulatory compliance, and successful delivery.
Drive innovation, platform modernization, data governance, root cause analysis, and continuous improvement initiatives to enhance business value and operational efficiency.
Requirements Proven experience delivering large-scale data, analytics, and decision-support solutions within banking or financial services environments. Strong expertise in business analysis, requirements management, solution design, data modelling, data governance, and regulatory compliance. Hands-on experience with modern data platforms such as Databricks, Snowflake, Azure Fabric, BigQuery, Hadoop, Spark, and related technologies. Proficiency in SQL and data analytics technologies, with working knowledge of Python and BI/visualization tools such as Power BI or QlikSense. ( Strong understanding of machine learning, NLP, AI governance, explainability, data lineage, and regulatory controls for AI-enabled solutions. Experience with data integration, data quality management, metadata management, data lineage, reconciliation, and enterprise data architecture. Interested candidates please email your latest resume to (HIDDEN TEXT)
Requirements Proven experience delivering large-scale data, analytics, and decision-support solutions within banking or financial services environments. Strong expertise in business analysis, requirements management, solution design, data modelling, data governance, and regulatory compliance. Hands-on experience with modern data platforms such as Databricks, Snowflake, Azure Fabric, BigQuery, Hadoop, Spark, and related technologies. Proficiency in SQL and data analytics technologies, with working knowledge of Python and BI/visualization tools such as Power BI or QlikSense. ( Strong understanding of machine learning, NLP, AI governance, explainability, data lineage, and regulatory controls for AI-enabled solutions. Experience with data integration, data quality management, metadata management, data lineage, reconciliation, and enterprise data architecture. Interested candidates please email your latest resume to (HIDDEN TEXT)