Top Data Jobs in Singapore

Own logistics analytics problems end to end, delivering insights and advanced analytical solutions for fleet sizing, routing, staffing, demand planning, and operational optimization. Apply simulation, optimization, statistical modeling, causal inference, and experimentation methods. Build scalable data products, collaborate with engineering and data teams, mentor junior analysts, and communicate recommendations to senior stakeholders.

Supports enterprise data engineering initiatives, including developing and maintaining data pipelines, preparing and validating data, improving data quality, automating manual processes, analyzing and reporting data, and exploring AI-driven solutions. Collaborates with engineering and business teams to gather requirements, translate them into data solutions, conduct testing, document work, and support continuous improvement across enterprise-scale banking data platforms.

Supports regional Disaster Recovery initiatives through incident and exercise data analysis, dashboard development, reporting, documentation, checklist improvement, knowledge management, and automation. The intern will help improve recovery readiness, operational resilience, process efficiency, and decision-making while collaborating with technology teams across the organization.

Leads cross-functional special projects involving air network planning, scheduling, airline partner performance, and operational analytics. Develops optimization models, analytical frameworks, automation tools, scripts, and lightweight products to improve routing, capacity allocation, scheduling efficiency, and planning workflows. Partners with engineers, operations teams, and management to implement solutions, establish best practices, and scale innovative approaches across the organization.

Build and maintain scalable data pipelines for scientific, sensor, and operational data used in machine learning. Responsibilities include feature engineering, data quality monitoring, versioning, lineage, drift detection, data contracts, governance, sensor ingestion, synthetic data infrastructure, and MLOps data-layer components such as feature stores and dataset registries.

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

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

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

Lead development and deployment of deep learning and probabilistic models for multi-physics engineering problems. Architect scalable data pipelines, collaborate with simulation engineers, drive R&D, mentor junior team members, and work directly with customers to integrate models into production, including onsite customer collaboration and presentations.

Leads mechanical engineering for critical infrastructure and data center projects across APAC. Reviews and optimizes mechanical, plumbing, and fire protection designs; coordinates consultants, contractors, vendors, and cross-functional teams; supports thermal, hydraulic, CFD, and acoustic studies; manages technical documentation, quality reviews, scope changes, schedules, costs, and compliance with applicable standards.

Build and operate scalable scientific data pipelines and services integrating laboratory, OMICS, imaging, assay, formulation, consumer, and performance data. Implement metadata, provenance, versioning, identity resolution, access, semantic, and data-quality controls. Develop interfaces connecting repositories, knowledge graphs, analytical tools, computational models, and AI workflows. Monitor reliability, availability, dependencies, and performance while resolving bottlenecks and providing operational support for trusted analytical data foundations.

Build and operate scalable financial market data pipelines for Binance stock products and AI applications. Responsibilities include sourcing, ingesting, modeling, processing, storing, and serving real-time and historical data; designing multi-market data frameworks; optimizing Flink pipelines; and establishing quality, reconciliation, monitoring, lineage, and recovery systems. The role also evaluates vendors and data sources, defines data contracts, and collaborates with trading, product, AI, legal, and compliance teams.

Conduct technical research at the intersection of AI and blockchain, exploring LLMs, agents, on-chain data, trading, and intelligent applications. Build reproducible prototypes and validate innovative ideas through proof-of-concepts and product demonstrations. Track industry developments, analyze research findings, document conclusions, and collaborate with algorithm and engineering teams to advance concepts toward implementation.

Leads architecture, security, operations, cost management, and governance for an enterprise GenAI and data platform on Google Cloud. Designs LLM agents, RAG solutions, secure tool-enabled workflows, private networking, IAM, observability, and capacity models. Oversees distributed data pipelines, cloud migration, performance optimization, risk approvals, and engineering standards while mentoring teams and engaging security, vendors, and business stakeholders.

Own the data platform product roadmap, including multi-chain ingestion, on-chain parsing, token and market data, APIs, datasets, analytics tools, governance, quality monitoring, and metric frameworks. Partner with engineering, backend, algorithm, BI, risk, growth, and business teams to deliver reusable data products. The role requires substantial product management experience, hands-on Web3 knowledge, understanding of blockchain data models, SQL proficiency, and familiarity with data infrastructure and analytics tooling.

Leads mechanical engineering design delivery for hyperscale and co-location data center projects. Oversees technical quality, project objectives, schedules, resources, budgets, and multidisciplinary coordination. Builds client partnerships, supports bids and proposals, contributes to thought leadership, and drives sector growth. Requires extensive data center or mission-critical engineering experience, commercial acumen, professional engineering qualifications, Uptime Institute ATD certification, and eligibility for Baseline or higher AGSVA clearance.

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.

Leads the Markets Operations control, country oversight, data governance, regulatory assurance, performance reporting, and strategic control transformation agenda. Establishes control frameworks, manages operational risks and remediation, oversees data quality and analytics, ensures regulatory and service-level compliance, and partners with Risk, Compliance, Audit, Technology, and Change teams. Builds and leads specialist teams while promoting operational resilience, transparency, automation, and continuous improvement across multiple jurisdictions.

Develops and owns end-to-end machine learning models, from ideation and prototyping through implementation. Responsibilities include building predictive models for credit scoring and recommendation engines, researching model improvements using large datasets, and advancing data science capabilities. Collaborates with analysts and machine learning engineers. Optional experience includes alternate data for credit risk and deep learning or NLP model development.

Develops and owns end-to-end machine learning models, from ideation through implementation. Responsibilities include prototyping credit-scoring and recommendation models, researching improvements using large datasets, and advancing the organization’s data science capabilities. Collaborates closely with analysts and machine learning engineers while applying quantitative, statistical, and business expertise.

Design, build, and maintain data models, ETL pipelines, dashboards, and APIs to support industry models, research, and consulting. Ensure data accuracy, completeness, and observability while integrating new datasets and cloud infrastructure. Collaborate with analysts and stakeholders to deliver robust, modular production data systems with minimal supervision.

Partner with APAC Compliance teams to deliver regulatory reporting, compliance monitoring, risk analytics, and assurance reporting. Build scalable data models, dashboards, and self-service analytics tools while validating regulatory submissions and supporting automated monitoring. Maintain data quality, governance, and documentation, and collaborate with Risk, Compliance, Product, and Engineering stakeholders. The role requires strong SQL and data modeling skills, with opportunities to use dbt, modern data warehouses, data engineering tools, and Python.

Lead Databricks’ enterprise sales organization across Malaysia and Indonesia, managing Enterprise Account Executives and owning team quota attainment. Develop strategic revenue plans, grow consumption among strategic financial services accounts, build executive customer and partner relationships, and translate technical data and AI capabilities into business value. The role requires Bahasa fluency, extensive enterprise software sales leadership, strong Data and AI expertise, hyperscaler relationships, and a proven record of developing high-performing teams.