Senior AI Researcher - Global Asset Management Firm - Singapore

Our client, a global asset management firm, are actively looking for a Senior AI Researcher to lead applied machine learning research that informs portfolio construction, alpha generation, risk forecasting, and trade execution. This is a hands‑on research role, where you will develop and validate ML/AI models, translate research into production-ready components with quant engineers.

The role:

• Lead end-to-end ML/AI research projects addressing alpha signals, factor discovery, risk modeling, trade signal generation, or operational analytics.
• Develop, validate, and benchmark models (supervised, unsupervised, representation learning, time-series deep learning, graph models, reinforcement learning where applicable).
• Design robust data pipelines, feature engineering, and model evaluation frameworks suited for financial time series including walk-forward validation, nested CV, and back testing with transaction-costs and realistic execution assumptions.
• Collaborate with portfolio managers, quant developers, and data engineers to productionize models, define monitoring/alerting, and ensure model governance.
• Conduct rigorous statistical and economic significance testing; quantify model robustness to regime shifts and data snooping.
• Communicate findings clearly to technical and non-technical stakeholders (research notes, presentations, reproducible notebooks).

What you offer:

• PhD or MSc (strong preference for PhD) in Machine Learning, Statistics, Computer Science, Applied Math, or a quantitative discipline with relevant research experience.
• 1+ years industry experience applying ML/AI to time-series or structured data; experience in asset management, hedge funds, or prop trading is strongly preferred.
• Strong programming skills in Python and ML libraries (PyTorch, TensorFlow, scikit-learn). Experience with data tooling (Pandas, NumPy), model ops (MLflow, BentoML), and cloud platforms (AWS/GCP/Azure) desirable.
• Demonstrable experience in sequence models (RNNs, Transformers), graph neural networks, probabilistic models, or reinforcement learning applied to finance or related domains.
• Deep understanding of backtesting best practices, overfitting controls, transaction cost modeling, and walk-forward validation methods.
• Experience collaborating with software engineers to productionize models; familiarity with containerization (Docker), CI/CD, and scalable data infrastructure.
• Strong statistical rigor, experimental design, and ability to explain model limitations and failure modes.
• Excellent communication skills and ability to influence portfolio decisions with evidence-based research.
• Prior publications or open-source contributions in ML/finance.

The sell:

• Competitive compensation with performance-linked incentives and benefits.
• Opportunity to work on large, clean datasets and deploy research at institutional scale.
• Collaborative, multi-disciplinary environment with access to trading, risk and data science teams.