Top Machine Learning Jobs in Singapore

Manage applied research programs and external partnerships in Singapore, establishing roadmaps, governance, operating rhythms, milestones, and reporting. Partner with research leaders, engineers, executives, and collaborators to resolve ambiguity, manage risks and dependencies, coordinate resources, and connect technical work to strategic outcomes. Support research outputs, cross-regional collaboration, team development, and high-performing ways of working across concurrent initiatives.

Supports the development, testing, documentation, and deployment of AI-powered automation solutions. Identifies business processes for automation, collaborates with business and technology teams to gather requirements and track projects, and prepares reports and documentation. The role also contributes to digital transformation initiatives while applying analytical, problem-solving, communication, and teamwork skills.

Owns infrastructure service delivery and reliability for global Wealth Production Management. Partners with infrastructure, architecture, and application teams to improve resiliency, monitoring, end-user performance, capacity, and operational effectiveness. Leads incident, problem, and change management activities; coordinates cross-functional delivery; supports infrastructure projects; identifies cost-saving and right-sizing opportunities; and applies AIOps, machine learning, predictive maintenance, and generative AI to improve service performance and governance.

Leads infrastructure service delivery and reliability for Wealth Production Management. Partners with infrastructure, architecture, and application teams to improve resilience, performance, telemetry, monitoring, capacity, and end-user experience. Coordinates incident, problem, and change management, supports technology and regulatory projects, identifies infrastructure cost savings, and operationalizes AIOps, machine-learning event correlation, predictive maintenance, and generative AI practices while delivering service, financial, and governance KPIs.

Develop machine learning models for credit scoring, risk profiling, fraud detection, and customer segmentation. Perform feature engineering, model validation, performance monitoring, portfolio analysis, and drift investigation. Build reproducible training, scoring, and monitoring pipelines using version-controlled code. Present analytical findings clearly and manage multiple projects independently.

Lead the strategy, development, delivery, governance, adoption, and optimization of AI-enabled products for enterprise and financial-services use cases. Translate business needs into roadmaps, requirements, and measurable outcomes while partnering with engineering, data science, design, risk, cybersecurity, compliance, and governance teams. Engage regional clients and stakeholders through discovery sessions, workshops, and demonstrations. Ensure responsible AI, privacy, security, resilience, and regulatory standards while tracking product value, adoption, and performance.

Leads financial planning, budgeting, forecasting, performance management, controllership, and risk assessment for ANZ and ASEAN SME&C Corporate. Partners with business leaders to provide actionable financial insights, manage revenue and expense forecasts, track KPIs, explain variances, allocate investments, and improve decision-making. Uses Power BI, Copilot, machine learning, and AI-assisted forecasting to automate reporting and strengthen forward-looking analysis.

Lead technical discovery, solution design, demonstrations, and proof-of-concept support for Anaconda’s AI and data science platform across APAC. Partner with account executives, customers, regional partners, and internal teams to advance complex sales opportunities, communicate business value, provide customer feedback, and support successful handoffs. The role requires customer-facing technical expertise, independent prioritization, regional collaboration, and travel to customer sites as needed.

Leads a team developing and productionizing predictive, machine learning, deep learning, and GenAI solutions for semiconductor manufacturing and HBM product engineering. Responsibilities include yield and reliability modeling, large-scale data pipelines, MLOps, model monitoring and drift control, distributed analytics, deployment, and engineering decision support. The role also requires mentoring engineers, setting technical direction, collaborating cross-functionally, and translating model outputs into improvements in quality, cost, cycle time, and manufacturing efficiency.

Research intern developing condition-based predictive maintenance for critical systems. Responsibilities include collecting, cleaning, transforming, and annotating data; evaluating and developing supervised and unsupervised machine learning models; and creating software to automate the machine learning lifecycle. The intern will deliver an end-to-end working system, documentation, test scenarios, and team presentations or workshops while collaborating with engineers, data scientists, and domain experts.

Conducts interdisciplinary neuroscience and artificial intelligence research by collecting and analyzing neural or behavioral data, developing experimental and computational workflows, and contributing to presentations, grants, publications, and collaborative lab activities.

Leads domain pre-sales for credit and fraud risk solutions across financial services and other industries. Partners with clients to identify challenges, develop proposals, business cases, demos, and executive presentations, and support negotiations and go-to-market strategy. Acts as a subject matter expert across the risk lifecycle, payment ecosystems, decisioning platforms, and digital transformation. Builds thought leadership through events, content, webinars, and presentations, with up to 50% regional travel.

Lead AI transformation engagements across APAC, translating business opportunities into enterprise AI strategies, prototypes, production-ready solutions, and measurable outcomes. Build and deploy GenAI applications, AI agents, RAG solutions, intelligent workflows, and automations while ensuring security, governance, scalability, and adoption. Engage senior stakeholders, support proposals and business development, develop reusable methodologies, and coach consultants.

Leads APAC AI transformation engagements, enterprise AI strategy, prototypes, AI agents, RAG solutions, and scaled production deployments. Advises senior executives, drives adoption and value realization, manages multidisciplinary teams, develops AI offerings and methodologies, supports business development, and mentors consultants and technical specialists.

Conduct ML research (deep learning, NLP) on large and unstructured financial datasets, implement algorithms in high-quality code, back-test models, document findings, and collaborate with senior researchers during an 11-week summer internship in Hong Kong or Singapore.

Lead modelling and analysis roadmap for FX products: forecast and monitor FX volatility, optimise trade execution with statistical/ML models, run customer and profitability analyses, and partner with product, trading, risk, and engineering to translate insights into trading and product decisions.

Leads Olea’s Risk & Decisioning function, building quantitative credit risk models, AI-enabled decision systems, and trusted decision infrastructure. Establishes model and agent governance, validation, explainability, auditability, controls, and lifecycle standards. Partners with risk, commercial, legal, compliance, operations, product, engineering, and data teams to operationalize decisioning capabilities and deliver measurable business outcomes. Mentors technical and analytical teams while translating risk appetite and business priorities into scalable products and roadmaps.

Analyze preclinical and clinical imaging, biobank, and cohort data to support drug discovery. Develop imaging biomarkers and AI/ML-enabled image analysis algorithms, conduct literature and database research, collaborate with external research partners, and maintain data integrity, documentation, and compliance.

Develops and closes enterprise AI solution deals across APAC, targeting media, fintech, and logistics organizations. Builds sales pipelines, leads consultative discovery, maps business challenges to AI solutions, negotiates complex contracts, and develops partnerships with cloud providers, systems integrators, and technology vendors. Collaborates with solution architects and marketing while explaining AI concepts, risks, privacy, and ROI to technical and business stakeholders.

Provides second-line oversight and credible challenge for AI, privacy, and data risks across APAC. Leads monitoring and testing reviews, control assessments, regulatory engagements, issue remediation, governance reviews, and executive reporting. Translates regulatory expectations into controls, testing methods, evidence requirements, and practical guidance while partnering with business, technology, legal, data, cybersecurity, and model risk stakeholders.

Own the strategy, roadmap, development lifecycle, and go-to-market activities for fraud analytics products. Lead customer discovery, prioritize product improvements, analyze complex data, collaborate with development and data science teams, and use market and customer insights to improve user outcomes. The role requires fraud prevention expertise, statistical modeling and AI/machine learning knowledge, strong stakeholder communication, and flexible UK-US working hours.

Leads Wise’s APAC product analytics function and a team of approximately 10 analysts. Partners with Product, Banking, and Engineering leaders to shape regional roadmaps, define KPIs, measure impact, generate strategic insights, and improve customer experiences. Responsibilities include developing analytics strategy, coaching analysts, building trusted cross-functional partnerships, and driving data-led organizational change. The role also involves hands-on SQL and Python or R analysis, with potential application of AI and machine learning techniques.

Translate stakeholder challenges into data science projects; clean and preprocess structured and unstructured data; perform feature engineering and statistical analysis; build, deploy, and refine machine learning models; create dashboards and visualizations; communicate insights through compelling data stories; and provide actionable recommendations while managing project timelines and stakeholder relationships.

Translate stakeholder business challenges into data science projects; clean and preprocess structured and unstructured data; perform statistical analysis, feature engineering, and machine learning; deploy models; create dashboards and visualizations; communicate insights and recommendations; and manage stakeholder feedback and project timelines.

Leads a data annotation team supporting autonomous vehicle perception and machine learning models. Oversees daily operations, productivity, quality, SLAs, workflow efficiency, hiring, onboarding, performance management, coaching, and employee development. Uses data-driven problem solving and automation to remove bottlenecks. May manage external vendors, annotation metrics, and projects across time zones.