Top Data Jobs in Singapore

The Enterprise Account Executive, Acquisition role focuses on developing a sales strategy within a territory for new customer acquisition, meeting sales targets, and collaborating with Solutions Architects and Professional Services teams.

Lead credit risk analytics for business lending: monitor portfolio performance, identify emerging risks, support portfolio-level credit strategies, prepare reports and dashboards, collaborate with operations and product, contribute to stress testing, regulatory reporting, and governance.

Drive data science for Coinbase consumer products by analyzing ambiguous business problems, defining metrics, building experimentation and causal frameworks, developing advanced models, maintaining ETL pipelines, and translating insights into product strategy. The role requires SQL and Python expertise, statistical rigor, data visualization, stakeholder influence, code reviews, and responsible use of generative AI tools.

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.

Leads enterprise data governance and management programs for banking and financial services clients. Responsibilities include developing governance frameworks and artifacts, managing metadata, lineage, data quality, MDM, architecture, sovereignty, residency, and regulatory compliance initiatives. The role partners with executives, regulators, risk, compliance, business, and technology teams, delivers Agile data transformation programs, and supports governance reporting. Singapore work authorization is required, with no Employment Pass sponsorship available.

Lead data governance and management consulting engagements for financial services clients. Design governance frameworks, maintain data dictionaries, glossaries, metadata catalogs, lineage, and classification standards, and support data quality, architecture, MDM, and regulatory compliance initiatives. Partner with executives, risk, compliance, technology, and business stakeholders while delivering Agile data transformation programs and governance reporting using enterprise platforms such as Collibra, Informatica, and Microsoft Purview.

As a Data Scientist at Micron, you'll develop AI-driven solutions in semiconductor manufacturing, build predictive models, and communicate analytical insights to stakeholders.

Design and implement advanced scheduling, optimization, and simulation solutions in manufacturing to improve production metrics and drive innovation.

Develop machine learning, simulation, and optimization models to analyze manufacturing, equipment, and process data. Build scalable analytics products, dashboards, and automated workflows. Partner with cross-functional engineering teams to improve equipment efficiency, factory productivity, and drive operational excellence through data-driven insights and recommendations.

The role involves designing software and algorithms to analyze large datasets for actionable insights, enhancing production processes and product development.

Design and deploy computer vision and deep learning models (CNN, LSTM, transformers) for industrial inspection and anomaly detection; extract, clean, and analyze large multimodal datasets; implement MLOps (MLflow, Airflow, Docker) for production deployments; collaborate across engineering, operations, and quality teams; mentor engineers and contribute research outputs and technical documentation.

Design and implement analytics, simulation, optimization, and digital twin solutions for semiconductor manufacturing. Develop software, models, and AI/LLM-enabled tools to analyze large unstructured data, optimize operations, and communicate insights to product and operations teams while driving global collaboration and continuous improvement.

The Senior Data Scientist will improve semiconductor quality through predictive models and AI solutions, mentor juniors, and communicate insights effectively.

Hands-on learning internship exposing the candidate to semiconductor wet-process manufacturing, process flows, equipment behavior, and basic manufacturing data analysis. Support data review, evaluate process stability, document observations, participate in technical discussions, and deliver a final report and presentation summarizing analysis, findings, and learned concepts.

Develop and deploy product-disposition workflows and deviation alert systems using statistical tools and ML/AI to improve yield, reliability, and quality. Build algorithms for data mining, pattern recognition, and wafer/die screening. Collaborate with Fab, design, and quality teams, mentor team members, and drive technical innovation to meet KPIs like quality, cost, cycle time, and scale.

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

Design, implement, and maintain data-driven processes and analytics solutions. Collect, cleanse, automate, and prepare data from disparate sources. Build KPIs and dashboards, apply statistical and machine learning methods, collaborate with stakeholders, perform testing and deployment, and train users to enable data-driven decision making and process improvements.

As a Computer Vision Engineer, you will develop image processing solutions, apply machine learning techniques, and collaborate on engineering projects to enhance manufacturing innovation.

As a Senior Data Scientist, you'll analyze large datasets using advanced mathematical and statistical methodologies to derive actionable insights and optimize semiconductor operations.

The role focuses on developing product disposition solutions, enhancing yield and reliability through data analysis, and mentoring team members while collaborating across teams.

The IT Engineer (Data Services) is responsible for database system management, performance tuning, compliance, incident resolution, and automation of database operations.

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 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.