Machine Learning Quant

About the role

At Da Vinci Trading, we're not just building trading systems, we're building the future of intelligence in the markets. Our teams of researchers, quants, engineers and traders collaborate to push the boundaries of what's possible. If you're passionate about machine learning, statistical modelling, and want your work to directly impact performance in real‑world financial markets, you'll feel right at home here.

Responsibilities

• Develop, test, and deploy novel ML/AI models for prediction, signal generation, and anomaly detection.
• Work closely with the Quant Research and Trading Intelligence teams to translate insights into live strategies.
• Explore state‑of‑the‑art techniques (e.g., deep learning, reinforcement learning, graph neural networks) and rigorously evaluate their applicability in financial domains.
• Take full ownership of your models and strategies - from signal research, feature engineering, and backtesting through to execution, live performance monitoring, risk assessment, and iterative improvement in production.
• Analyze large, noisy, high‑frequency data streams; performing advanced feature engineering, and bias detection.
• Collaborate with our systems and infrastructure engineers to ensure models are productionised efficiently and reliably.

Requirements

• A strong academic background in Machine Learning, AI, Statistics, Computer Science, Mathematics, or a related field - typically a PhD or equivalent, or an MSc with significant relevant experience
• Hands on experience building and deploying ML/AI models, especially for time series forecasting or anomaly detection.
• Genuine curiosity about trading, market microstructure and financial dynamics
• Proficiency in Python and common ML frameworks (e.g., PyTorch, TensorFlow, scikit‑learn).
• Solid programming and software development skills, with experience in a production environment.
• Experience with core data and infrastructure tools like Docker, S3/MinIO, and PostgreSQL/OLAP databases.
• A deep, intuitive understanding of topics like overfitting, generalization, and feature engineering.
• Stays up to date on ML/AI literature and experiments with new ideas
• The ability to communicate complex ideas clearly and collaborate effectively in a high‑performance team.
• MLOps pipelines and tools (e.g., MLFlow, ClearML, Weights & Biases).
• High-performance computing and GPU optimization (e.g., CUDA, TensorRT).

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