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Quantitative Researcher - Cash Equities - APAC Region
Anson McCade have partnered with a globally-renowned prop trading firm which is hiring Quantitative Researchers for a new research and trading team covering global cash equities, particularly APAC markets. The team is led by several Senior Quantitative Researchers/Portfolio Managers with extensive experience and strong track records within this space.
The successful candidate will be responsible for collaborating on the full research and trading lifecycle for systematic strategies at HFT/short-term, intraday or mid frequency time horizons (seconds up to 1-2 weeks). You will cover global equities markets, covering data pre-processing and feature engineering on market/alternative datasets, through to putting models into production and monitoring their performance in live trading, in collaboration with other quant researchers, developers and monetisation experts in the team.
The Role:
• Research, develop and monitor HFT, intraday or mid-frequency cash equities or futures strategies.
• Develop and optimise infrastructure and tools on an ad hoc basis.
• Lead more junior members of the team and support the Senior Quant Researchers/Traders, with the aim of progressing into a sub-PM/Lead Quant Researcher role.
Requirements:
• At least 2 years of experience in front office quant research in equities markets, particularly in the APAC region.
• Proficiency in Python, experience with C++ is desired.
• A Bachelor's and/or Master's degree from a top University, PhDs are preferred but not required.
Job ID: 153524259
Skills:
SQL Server, Python, Bloomberg
Skills:
Machine Learning, Python, Statistical Techniques
Skills:
Java, Machine Learning, C, Scala, Statistical Modelling, Clustering, Sql, Pattern Recognition, Python, Optimisation, Feature Engineering, Model Evaluation, Time-Series Analysis, R, Data Processing, Simulation, backtesting
Skills:
Tensorflow, Numpy, Pandas, Pytorch, Python, scikit-learn
Skills:
Machine Learning, Apis, Natural Language Processing, Sql, Data Extraction, Numpy, Pandas, Python, cloud-based data infrastructure, scikit-learn, automated data pipelines, Econometrics, alternative-data analysis, statsmodels, time-series analysis, statistical methods