Quantitative Researcher - Equities/Futures HFT

A distinguished portfolio manager has recently joined one of our partners - one of the world's largest investment management firms. To build a robust team for the future, the PM is seeking out skilled Quantitative Researchers with a strong foundation in a numerically intensive academic discipline. As a Quantitative Researcher, you'll be working in unison with Quant Developers, the PMs and Quant Traders. In the day-to-day, you'll be working on devising and improving automated trading strategies. Moreover, you'll be exploring large datasets and employing a wide range of innovative Machine Learning technologies. Our partner is eager to connect with academically gifted candidates that have a keen interest in global financial markets.

Requirements

A PhD/PostDoc in a numerically intensive STEM subject (i.e., Computer Science, Engineering, Mathematics, Physics (Astrophysics, Geophysics et al.), Machine Learning, Statistics, and comparable scientific disciplines

Strong analytical and mathematical skills

A passion for developing quantitative trading strategies as well as analysing and drawing conclusions from large datasets

Degree from a top-tier university (i.e., Oxbridge, Ivy League, CALTECH/Georgia Tech, KTH, Ecole Polytechnique, National University of Singapore, ANU/Melbourne/Auckland, University of Tokyo, UvA, University of Science and Technology, Russell Group, ETH Zurich/EPFL, U15, Technion, Paris-Saclay/ENSAE, TU9)

Fluency in C++, Python (PyTorch, scikit-learn, TensorFlow, etc.) AND/OR Java

Desirables

Academic excellence alongside publications at conferences or in top-tier journals

Any other relevant coding languages including MATLAB, C#, R or Linux

Experience with Machine Learning, Natural Language Processing or Deep Learning

Experience working in a fast-paced environment

Internships within Quant Finance (Equities, Derivatives, Index Rebalancing, HFT, Alpha Generation et cetera), Tech, AI, or related fields

Participation in STEM Olympiads AND/OR Programming Competitions (e.g. KAGGLE)