Top Data Jobs in Singapore

Own commercial strategy and operations for enterprise pharma and AI accounts: drive pilots-to-revenue, account expansion, pipeline management, pricing, forecasting, and deal strategy. Build cross-functional relationships, push deals through legal/procurement/IT, act as CEO counterpart to shape strategy and product roadmap, and be highly present in person with customers and at conferences.

Lead design and implementation of cloud DevOps strategy for global Data and AI infrastructure. Build observability and detection capabilities, enable self-service tooling for data teams, manage scalable, secure infrastructure, and align DevOps practices and standards across geographies.

Design and implement smart manufacturing AI solutions for advanced factory scheduling, optimization, and simulation. Build analytics and applications, apply new algorithms, collaborate across global sites, drive benchmarking, and support continuous improvement of production metrics.

The Data Scientist will optimize semiconductor processes, analyze data, develop visualization tools, and support production through advanced analytics and modeling.

The role involves developing advanced data analytics solutions for yield and quality improvement in HBM product engineering, collaborating with cross-functional teams and driving innovation in AI and machine learning.

The Data Scientist will analyze big data to develop predictive models and actionable insights, collaborating with various teams to enhance products and services.

Lead procurement data strategy to enable agentic AI: design agent-embedded governance, define agent-consumable data requirements, build lineage/catalog foundations, establish machine-readable policies and feedback loops, run experiments, and coach cross-functional stakeholders to operationalize master data and data quality at scale.

The Principal Data Scientist will optimize semiconductor yields, develop data pipelines, apply advanced analytics, support experimentation, and communicate findings effectively.

Serve as ASOs single front door for Operations demands across Asia, translating business needs into prioritized initiatives, managing demand pipeline, shaping improvement using AI/data/APIs, and coordinating stakeholders to deliver operational efficiency, compliance, and value realization.

Lead end-to-end development and productionization of predictive, prescriptive, and agentic AI analytics across commercial, marketing, finance, and franchise domains. Build scalable ML/LLM solutions, implement RAG and orchestration, partner with stakeholders, and drive adoption and business value through analytics products and MLOps practices.

Lead analytics for quick commerce growth across markets: design causal impact and ROI measurement, build BigQuery/dbt SQL models, produce dashboards and agentic AI solutions, influence senior stakeholders, and mentor junior analysts.

Design, deploy, and maintain highly available Kubernetes clusters and GitOps workflows. Build CI/CD pipelines, implement IaC, monitoring, and SRE practices (SLIs/SLOs, error budgets). Lead incident response and automation to reduce toil while collaborating with development and data teams.

Lead application of data science, machine learning and generative AI to drive SME customer lifecycle outcomes. Translate business problems into models, build and deploy scalable production ML/AI solutions, partner with stakeholders and data engineering, monitor model performance, and promote responsible AI and MLOps best practices.

Manage and mentor a team of data scientists/analysts to build data-driven solutions: oversee data collection, cleaning, modeling, ML development, SQL reporting, microservices/API deployments, visualization, and lifecycle management from pilot to production.

Lead functional business analysis for enterprise KYC/AML capabilities across Group Technology & Ops. Manage stakeholder relationships, define capabilities, translate business requirements into functional and non-functional requirements, oversee testing and scope control, support deployments and post-go-live knowledge transfer, and lead and develop a team of functional business analysts.

Assist in building and maintaining data ingestion pipelines and storage for AI workflows. Implement deployment, security, and monitoring for applications; collaborate with stakeholders and design teams; test and validate data verification and AI workflows; document solutions and follow best practices while continuously learning new tools.

Support the AI team to build generative AI and computer vision solutions: code in Python, explore and analyze datasets, identify patterns, report findings, and implement models using PyTorch, TensorFlow, scikit-learn, and pandas.

Lead and mentor a logistics analytics team to deliver insights on fleet performance, delivery efficiency, and cost optimization. Shape analytics roadmap, design performance metrics and AI data products, apply decision science and operations research, manage end-to-end analytics execution, and present executive-ready recommendations to drive measurable improvements.

Build and productionize ML systems for personalization, search, and recommendations. Apply statistical and ML models using Python and SQL, collaborate with stakeholders, and measure business impact across cloud environments.

Operate, maintain, and optimize hybrid cloud Data and AI platforms (Cloudera CDP/CML, Hadoop, Spark) across AWS and on-prem. Perform platform upgrades, security patching, container/Kubernetes/OpenShift administration, monitoring, troubleshooting, and automation using Shell/Python. Collaborate with Infrastructure, Security, SRE, and Data/ML teams to ensure availability and scalability of AI/ML and Generative AI services (SageMaker, EMR, Bedrock).

Support the Data Science team to design, develop, deploy, and maintain analytics and ML solutions for manufacturing. Responsibilities include data collection/cleansing, EDA, predictive modeling, stakeholder collaboration, production deployment support, and presenting results to leadership.

Analyze casino operations and product performance, build models and ML solutions for floor optimization, demand forecasting and manpower scheduling, create dashboards and A/B tests, collaborate with Operations, Marketing, BI, and IT to translate analytics into business strategy and operational improvements.

Lead a data insights team to deliver analytics, AI and automation solutions that drive measurable business outcomes. Partner with stakeholders to translate problems into analytical initiatives, manage end-to-end analytics lifecycle, operationalize AI-enabled capabilities, establish KPIs and reporting, and mentor data talent to build organizational analytics maturity.

Lead development and evolution of the Group's AI risk framework, translate policy into practical controls, engage business and tech teams, monitor AI use cases, guide resolution of control gaps, and align frameworks with regulatory expectations and industry developments.

The Associate Director will lead engineering strategy, oversee multiple teams, ensure quality delivery of applications, and promote a high-performing, innovative engineering culture.