Top AI & Machine Learning Jobs

Lead strategic and operational data science through quantitative analysis, forecasting, experimentation, and data storytelling. Mentor data scientists, advise cross-functional leaders, define metrics and forecasts, and build scalable decision-support systems. The role also contributes to AI and machine-learning systems that optimize product behavior, pricing, risk, allocation, and customer experiences, with an emphasis on rigorous validation, governance, and measurable outcomes.

Lead end-to-end design and rapid delivery of 0-to-1 fintech products with the Founders' Lab. Collaborate with executive leadership to define product direction, build MVPs, deploy to live users, iterate quickly, and balance rapid experimentation with sound backend architecture and occasional frontend/API work. Operate as a product-minded senior engineer in high-ambiguity, fast-paced environments.

Lead quantitative analysis, experimentation, and statistical modeling to guide product and business decisions. Build scalable analytics systems and decision frameworks, partner with cross-functional teams, define success metrics, and measure initiative impact. Communicate insights to executives, influence product strategy and roadmaps, and solve ambiguous problems using large datasets. The role also contributes to machine learning, AI, experimentation, and production decision-support systems.

Build and deploy predictive models, recommendation systems, LLM applications, incentive optimization frameworks, and causal inference models for customer growth. Partner cross-functionally to improve acquisition, engagement, retention, and expansion. Measure marketing impact, guide investment decisions, develop AI analytics and decision systems, and provide technical leadership and mentorship across data science initiatives.

Staff Data Scientist partnering with Growth, Marketing, Product, and Engineering teams to build data science solutions that drive customer acquisition, engagement, and retention. Responsibilities include predictive modeling, personalization, recommendation systems, LLM applications, incentive optimization, causal inference, marketing impact measurement, technical mentorship, and strategic leadership. The role requires deploying production machine learning systems and communicating analytical insights to senior stakeholders.

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.

Build and scale Airwallex’s machine learning platform for risk and fraud decisioning. Design data and model infrastructure, productionize training and serving workflows, improve experimentation, and optimize GPU-based training and inference. Partner with machine learning engineers, data scientists, product managers, and risk specialists to deliver reliable, low-latency systems. The role is based in Singapore and requires working from the office five days per week.

Leads Growth Data Science, partnering with executive, product, marketing, sales, finance, and regional teams to drive growth strategy. Oversees experimentation, causal inference, forecasting, segmentation, attribution, optimization, dashboards, and machine learning initiatives across the customer lifecycle. Establishes measurement frameworks, productionizes analytical models and intelligent tools, and manages a high-performing technical team while translating complex analyses into measurable business outcomes.

Lead analytical design for regulatory reporting and risk-compliance data infrastructure. Define data logic, models, validation frameworks, automated testing, and QA processes; investigate anomalies and audit findings; partner with compliance, finance, data, and engineering teams; shape scalable reporting platforms; and mentor analysts and data scientists.

Lead analytics and data design for regulatory reporting and financial crime compliance. Build data models, validation frameworks, automated testing, QA playbooks, and self-service analytics in partnership with Compliance, Finance, DataOps, and Engineering. Investigate anomalies and audit findings, identify root causes, shape risk analytics platforms, and promote analytical rigor and documentation across global teams.