Top Data Jobs in Singapore

Lead the development and deployment of Generative AI applications, collaborate with teams to deliver business use cases, and enhance data science skills.

Leads Binance’s market data function for its Traditional Finance expansion. Owns strategy, sourcing, licensing, vendor negotiations, integration, governance, entitlements, data quality, lineage, compliance, and budget management. Partners with product, engineering, legal, finance, exchanges, and vendors to build scalable market data infrastructure. Builds and mentors a team while serving as the internal expert on market data and market structure.

Develop and deploy machine learning models that improve e-commerce search relevance, recommendations, product classification, and customer intent understanding. Responsibilities include collecting and preparing large datasets, feature engineering, predictive modeling, validation, testing, and leading work in areas such as NLP, computer vision, information retrieval, and deep learning. The role requires building scalable, reusable machine learning pipelines and collaborating across concurrent projects.

The Technical Product Manager is responsible for product outcomes across the lifecycle, involving product vision, stakeholder engagement, backlog prioritization, and technical leadership.

The Transformation Lead for AI in APAC will manage change and drive adoption of AI tools across the studios, engage stakeholders, and run training programs.

The Enterprise BD Manager will expand and maintain key enterprise accounts, driving growth through strategic relationships and complex solution structures in maritime risk and sustainability.

Wayvia is building a talent pipeline for engineering roles focusing on backend systems, data pipelines, and product engineering. They seek curious and collaborative engineers to connect with and solve future challenges.

The AI Architect will develop AI solutions, support the global AI sandbox, provide technical guidance, and optimize AI products while collaborating with various stakeholders.

Lead technical strategy with channel partners across ASEAN to design, validate, and scale Microsoft Cloud and Data & AI solutions. Drive offer development, run workshops and POCs, enable specializations, and translate technical work into repeatable, revenue-generating partner services aligned to Microsoft Cloud and AI priorities.

Designs, develops, and maintains secure enterprise data pipelines and ETL/ELT processes integrating government and commercial systems. Ensures data quality, performance, availability, and real-time delivery for logistics, mission support, analytics, and operational decision-making. Responsibilities include database and pipeline optimization, cloud migration support, technical documentation, cybersecurity and governance compliance, troubleshooting, stakeholder coordination, operational exercise support, and mentoring junior personnel.

Migrate SQL Server data products and analytical workloads to a modern cloud platform. Redesign legacy SQL, ETL processes, and business logic; build scalable pipelines and analytical models; validate data quality; collaborate with stakeholders; and improve platform reliability, performance, cost efficiency, and AI-assisted data engineering automation.

Supports APAC implementation and operation of MUFG’s Data Governance framework. Oversees data quality, lineage, metadata, data dictionaries, key data elements, controls, reporting, risk remediation, audits, and regulatory requirements. Advises senior stakeholders across business, compliance, operations, technology, and risk; leads governance workstreams for transformations and system changes; facilitates regional forums; and mentors junior team members. Drives governance maturity, process improvements, automation, and adoption of data management standards.

Supports APAC data governance and data quality management activities across banking operations. Responsibilities include maintaining governance documentation, coordinating working groups, monitoring and analyzing data quality, investigating exceptions, tracking remediation, preparing management reports, and engaging regional and global stakeholders. The role also contributes to process improvement, reporting automation, testing, and AI-enabled initiatives.

Develop advanced AI, machine learning, forecasting, optimization, control, and predictive analytics solutions for building automation and energy management. Build mathematical models, simulations, digital twins, and scalable microservices; analyze industrial IoT data; and deploy solutions to cloud and edge environments. Collaborate with architects and business teams to integrate data-driven capabilities into products, identify growth opportunities, communicate customer value, and promote data science best practices. Mentor engineers and support globally distributed teams.

Develops advanced AI, machine learning, forecasting, optimization, control algorithms, mathematical models, simulations, digital twins, and predictive analytics for building automation and energy management. Analyzes industrial IoT data, deploys scalable machine learning solutions to cloud and edge environments, integrates algorithms into customer-facing platforms, mentors engineers, and communicates data-driven opportunities and outcomes to business teams.

Analyze sales channels, business performance, revenue, and acquisition trends; build dashboards and reports; design and calculate sales incentive programs; monitor targets; identify optimization opportunities; and present actionable insights to Sales, HR, and other stakeholders. The role requires cross-functional collaboration, banking industry knowledge, and proficiency in analytics, programming, visualization, and reporting tools.

Own analytical quality for a global manufacturing Control Tower, translating operational data into KPIs, forecasts, scenarios, early-warning signals, performance narratives, and recommendations. Frame complex decisions, apply statistics, forecasting, machine learning, and AI, validate models, monitor performance and data quality, and lead analytical workstreams with cross-functional stakeholders. Build prototypes using Python, SQL, R, BI tools, and cloud technologies while communicating insights and limitations to senior leaders.

Drive new business and expand existing accounts by defining sales strategies, developing pipeline (cold calling, email campaigns), managing end-to-end sales processes, coordinating vendors and inventory, maintaining sales reporting and forecasting, and sustaining vendor and partner relationships.

The Staff Data Scientist will optimize semiconductor manufacturing processes through data analysis, statistical modeling, and data visualization, collaborating closely with engineering teams.

Develop Green AI technologies focused on efficient machine learning, large language models, model optimization, parameter-efficient fine-tuning, retrieval-augmented generation, prompt engineering, and efficient inference. Conduct experiments, benchmark algorithms, analyze performance, contribute to research publications, and collaborate with multidisciplinary teams to translate research into deployable sustainable computing solutions.

The Senior Data Scientist will build and manage the intelligence layer of the Railway Common Data Platform, implement machine learning solutions, and enhance operational efficiency through data analytics and AI capabilities.

Drive enterprise sales for Semrush’s big data and emerging solutions. Serve as a subject-matter expert, partner with Account Executives on team selling, conduct product demonstrations, translate complex capabilities into customer value, identify expansion opportunities, provide product feedback, support launches and events, and maintain deep product knowledge. The role requires enterprise sales experience, solutions selling expertise, familiarity with DaaS, SaaS, or big data analysis, and strong storytelling skills.

Build, automate, and operate enterprise-scale data platforms using Databricks, Snowflake, Microsoft Fabric, Kubernetes, and cloud technologies. Responsibilities include Infrastructure as Code, CI/CD, GitOps, platform provisioning, reliability engineering, observability, security, governance, incident remediation, performance optimization, and FinOps. The role supports multi-environment deployments and self-service capabilities for data engineering and analytics teams.

Analyze global logistics, spend, invoice, and operational data to identify cost drivers, trends, anomalies, and optimization opportunities. Develop and maintain Tableau dashboards, automate reporting, support cost-to-serve models, and contribute to process mining and business intelligence initiatives using tools such as Celonis, Databricks, SAP, and Tableau. Collaborate with logistics teams worldwide, translate business requirements into analytics solutions, and present actionable findings to stakeholders.

Analyze and profile customer, KYC, onboarding, and reference data; design source-to-target mappings; define migration rules and validation criteria; identify data quality issues and anomalies; validate ETL pipelines; perform UAT, mock migration testing, reconciliation, and data verification; prepare technical documentation; and collaborate with business, compliance, governance, operations, and technology stakeholders to resolve migration issues.