Top Data Jobs in Singapore

Lead cost management for complex construction projects from initiation through completion. Responsibilities include cost planning, estimating, reporting, forecasting, value engineering, lifecycle costing, budget and risk management, contract administration, invoice coordination, stakeholder engagement, team leadership, client management, and business development. The role oversees multidisciplinary teams, ensures financial and delivery controls, presents cost plans and reports, and develops cost managers while supporting client objectives and business growth.

Develops actionable portfolio insights using complex datasets, dashboards, reports, and advanced analytics. Builds and deploys machine learning models, collaborates on predictive and prescriptive analytics projects, and owns initiatives from requirements gathering through deployment. Maintains documentation, improves data quality, and supports analytical data warehouses and tools. Requires strong SQL, R, Python, visualization, communication, and quantitative analysis skills, with Google Cloud and corporate real estate knowledge preferred.

Develop and productionize machine learning models across search, recommendations, content moderation, trust and safety, computer vision, NLP, and marketplace features. Responsibilities include data preparation, feature engineering, model training, evaluation, A/B testing, pipeline ownership, and deployment. The role also involves multimodal understanding, semantic retrieval, ranking, and direct analysis of Traditional Chinese, Cantonese, and English marketplace data while collaborating with product managers, engineers, and data scientists.

Own semiconductor manufacturing data analytics, quality screening, analytics platforms, data infrastructure, and automation across wafer sort, assembly, burn-in, final test, reliability, and quality operations. Lead yield learning, outlier detection, excursion monitoring, data governance, system integration, dashboards, ETL pipelines, and digital manufacturing initiatives while collaborating with engineering, foundry, OSAT, MES, and quality teams.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Develop and deploy predictive models and AI/agentic AI solutions using large-scale manufacturing data. Perform EDA, data extraction/cleansing, feature engineering, and statistical modeling. Collaborate with data engineers, manufacturing engineers, UX, and stakeholders to translate insights into operational improvements that boost yield, quality, efficiency, and business value.

Supports HR dashboard development, data visualization, reporting, data consolidation, quality assurance, analytics requests, process documentation, and reporting automation. The intern will transform data from enterprise HR and operational systems into actionable insights, improve reporting efficiency, and collaborate with HR stakeholders and technology partners on digital transformation initiatives.

Support OCBC’s Digital HR transformation by maintaining and testing Workday and other HR platforms, analyzing HR data, creating documentation, engaging stakeholders, and contributing to process improvement initiatives. The intern will assist with system enhancements, user acceptance testing, issue resolution, reporting, and digitalization projects that improve HR service delivery and employee experience.

Support HR technology platforms, including Workday, through system maintenance, configuration, testing, reporting, documentation, process improvement, and stakeholder engagement. Assist with HR data analysis, dashboards, digital transformation initiatives, workflow enhancements, and employee experience improvements while collaborating with HR, business, and technology teams.

Build and manage complaints and customer-friction data assets, including sourcing, validation, transformation, data modeling, thematic analysis, monitoring, anomaly detection, dashboards, and management reporting. Identify emerging customer risks, dissatisfaction drivers, and escalation signals through advanced analytics. Partner with stakeholders to improve data quality, customer-outcome oversight, governance, and decision-making while communicating actionable insights to senior leaders.

Develop and deploy machine learning, statistical modeling, and predictive analytics solutions for semiconductor manufacturing and business operations. Analyze large datasets, build scalable data workflows, conduct exploratory and root-cause analysis, and partner with engineering and software teams to operationalize models. Lead analytics projects, communicate insights to technical and business stakeholders, mentor junior data scientists, and ensure responsible, secure, compliant use of AI and data.

Leads procurement analytics, data science, business intelligence, and data governance initiatives, initially supporting the AMEA region. Develops reports, insights, predictive models, data quality rules, governance documentation, and scalable digital solutions. Partners with Procurement, Finance, Supply Chain, Technology, and external providers to improve data quality, automate reporting, enable analytics adoption, and translate complex findings into business recommendations.

Develop and maintain the bank’s Data and AI risk management framework. Conduct independent Line 2 reviews, assess data controls and quality processes, identify vulnerabilities, report findings, track remediation, evaluate data risk indicators, and support data governance initiatives while monitoring regulatory requirements and industry practices.

Audits and validates labeling annotation data for accuracy, consistency, guideline adherence, and regulatory compliance. Identifies errors and root causes, monitors quality KPIs, leads governance cycles, translates data trends into process improvements, and leads the NC Program. Collaborates with agencies and internal stakeholders across labeling operations.

Build interactive Power BI dashboards, extract and clean data, track operational KPIs, conduct market research, source prospective partners, and support pitch decks and strategic proposals. The intern will translate quantitative insights into executive communications while developing skills in business development, pipeline management, lead generation, stakeholder engagement, and data storytelling.

University students will gain hands-on experience developing technology solutions and applying AI, data science, machine learning, deep learning, GenAI, and MLOps to banking challenges. Responsibilities may include building LLM applications, developing models, implementing responsible AI controls, supporting cloud and DevOps initiatives, testing, deployment, and collaborating with cross-functional teams. The internship requires a minimum 12-week full-time commitment and includes assessments and interviews.

Manages quality, schedule compliance, installation standards, QA/QC documentation, and testing for BMS, BAS, and EPMS scopes across data center projects. Leads FAT, commissioning, and integrated systems testing while coordinating with construction, operations, IT, and network teams. Ensures multiple hyperscale projects meet delivery milestones and standardized quality requirements.

Maintain controlled static and reference data for Treasury platforms and downstream trade-processing activities. Responsibilities include creating and validating records, performing maker-checker controls, investigating exceptions, maintaining audit trails, supporting data-quality reviews, and coordinating with Markets, Operations, Risk, Compliance, Finance, Technology, and external providers. The role also supports projects, testing, process improvements, automation, procedures, training, and business continuity while meeting service levels, operational controls, and regulatory requirements.

Lead Data Scientist developing and maintaining payment-data models to detect fraud, scams, and money laundering across Asia-Pacific. Responsibilities include algorithm research, proof-of-concept development, product prototyping, financial crime model governance, client-facing visualizations, white papers, and regional customer support. The role collaborates with global data science and product teams while building locally tailored capabilities and considering privacy, security, regulation, model accuracy, and performance.

Leads advanced data analysis, develops dashboards, KPIs, and analytical models, and translates business problems into data-driven solutions. Works with complex datasets using Databricks, SQL, Python, BI tools, and cloud data platforms. Partners with engineering and governance teams, promotes analytics standards, mentors analysts, and communicates insights to stakeholders. Supports strategic decisions through descriptive, predictive, and prescriptive analytics.

Build and maintain data pipelines, transformations, curated datasets, dashboards, and analytical tools supporting pharmaceutical manufacturing and process operations. Investigate data-quality issues, translate business needs into reliable outputs, collaborate across scientific, engineering, manufacturing, and digital teams, and support testing, deployment, documentation, user adoption, and continuous improvement in a regulated environment.

Leads complex data engineering and analytics initiatives supporting pharmaceutical manufacturing and technical operations. Designs data pipelines, data flows, curated datasets, and scalable analytics solutions; improves data quality and accessibility; translates business needs into technical requirements; and drives adoption across scientific, manufacturing, engineering, and digital teams. Independently manages workstreams, stakeholders, risks, dependencies, prioritization, and value realization in a regulated environment.

Build and productionize machine learning models for semiconductor quality and manufacturing processes. Develop agentic AI, LLM, retrieval-augmented generation, tool-use, and memory capabilities for predictive quality workflows. Create maintainable analytical pipelines, optimize performance and token efficiency, and support testing, deployment, and monitoring. Communicate analytical findings to technical and non-technical stakeholders while applying AI tools responsibly.

Designs and validates data center network architectures for AI infrastructure, including BGP, EVPN/VXLAN, RoCEv2, QoS, and RDMA fabrics. Builds qualification labs, tests switches, NICs, and network operating systems, investigates performance and interoperability issues, troubleshoots congestion and packet loss, and develops documented standards, automation, and product feedback for customer deployments.

Implement and maintain data governance frameworks, policies, standards, and data quality controls. Support data ownership, stewardship, metadata management, lineage, regulatory compliance, governance forums, stakeholder engagement, training, audits, and enterprise data transformation initiatives for banking clients.