Description

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:

1. Lead end-to-end ML/AI research projects addressing alpha signals, factor discovery, risk modeling, trade signal generation, or operational analytics.
2. Develop, validate, and benchmark models (supervised, unsupervised, representation learning, time-series deep learning, graph models, reinforcement learning where applicable).
3. 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.
4. Collaborate with portfolio managers, quant developers, and data engineers to productionize models, define monitoring/alerting, and ensure model governance.
5. Conduct rigorous statistical and economic significance testing; quantify model robustness to regime shifts and data snooping.
6. Communicate findings clearly to technical and non-technical stakeholders (research notes, presentations, reproducible notebooks).

What you offer:

1. PhD or MSc (strong preference for PhD) in Machine Learning, Statistics, Computer Science, Applied Math, or a quantitative discipline with relevant research experience.
2. 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.
3. 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.
4. Demonstrable experience in sequence models (RNNs, Transformers), graph neural networks, probabilistic models, or reinforcement learning applied to finance or related domains.
5. Deep understanding of backtesting best practices, overfitting controls, transaction cost modeling, and walk-forward validation methods.
6. Experience collaborating with software engineers to productionize models; familiarity with containerization (Docker), CI/CD, and scalable data infrastructure.
7. Strong statistical rigor, experimental design, and ability to explain model limitations and failure modes.
8. Excellent communication skills and ability to influence portfolio decisions with evidence-based research.
9. Prior publications or open-source contributions in ML/finance.

The sell:

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