Drive machine learning innovation in quantitative trading by developing predictive models and high-frequency strategies, leveraging large-scale data, advanced deep learning, and collaborative research to deliver measurable market impact.

What You'll Do:

Join a high-performing quantitative research environment focused on developing sophisticated predictive models and delta-one trading strategies across APAC markets. You’ll work at the intersection of machine learning, quantitative finance, and trading technology to uncover statistically significant patterns in complex market data.

You’ll have the opportunity to work on challenging research problems where improvements to prediction quality can directly influence trading performance, while collaborating closely with traders, engineers, feature specialists, and fellow researchers.

Your responsibilities will include:

1. Develop and enhance deep learning models for high- to mid-frequency trading applications.
2. Analyse large-scale datasets to identify predictive signals and statistically robust market patterns.
3. Design and optimise sampling, weighting, transaction-cost modelling, targets, hyperparameters, and neural network architectures.
4. Apply CNNs, RNNs, LSTMs, transformers, and other modern deep learning approaches to quantitative problems.
5. Partner with traders and feature engineers to maximise the effectiveness of research inputs and model features.
6. Research emerging developments in machine learning, quantitative finance, and academic research.
7. Improve research methodologies, tooling, and modelling approaches across the wider team.
8. Apply distributed computing techniques to efficiently train models on very large datasets.
9. Collaborate with software and hardware engineers to transition research innovations into production.
10. Mentor junior researchers and communicate sophisticated quantitative and machine learning concepts clearly.

Why Join Us:

1. Work on challenging machine learning problems with direct applications in quantitative trading.
2. Collaborate across research, trading, software, and hardware disciplines.
3. Work with large-scale datasets and sophisticated computing infrastructure.
4. Explore cutting-edge developments in deep learning and quantitative modelling.
5. Contribute to models and research that can have significant real-world trading impact.
6. Be part of an established, international research environment built around collaboration, innovation, and continuous improvement.
7. Help shape the future direction of quantitative research, modelling, and research technology.

About You:

1. Graduate or postgraduate education from a leading university, ideally specialising in machine learning, statistics, mathematics, computer science, or another STEM discipline.
2. 3+ years of experience as a quantitative modeller or researcher, particularly within high- to mid-frequency delta-one trading.
3. Demonstrable experience developing and improving deep learning models for production environments.
4. Strong programming skills in Python or another relevant language.
5. Hands-on experience with PyTorch, TensorFlow, or another mainstream deep learning framework.
6. Strong understanding of CNNs, RNNs, LSTMs, transformers, and their respective strengths and limitations.
7. Experience working with large datasets and distributed computing environments.
8. Strong statistical reasoning and an ability to translate complex research into practical trading applications.
9. Excellent communication and collaboration skills, with the ability to explain complex technical concepts simply.
10. A curiosity-driven mindset and enthusiasm for solving difficult problems at the intersection of machine learning and quantitative finance.