Machine learning/deep learning

百奇星全球顶尖顾问·Human Resources Management / Consultancy

HK work permit required

3 to 5 yrs exp

Master

9.0 hrs/day, 5 days/wk

Salary negotiable

HK $80K-140K/Month

Job Highlights

博士優先，深入掌握深度學習

需在頂級會議發表多篇論文

完整線上深度學習建模經驗

Job benefits

Competitive pay

Discretionary bonus

Year-end bonus

Flexible working hours

Job Description

Job Responsibilities

In the various market data involved in quantitative trading, conduct high-altitude innovative deep learning model research, explore the laws and predictive ability behind the data;
Design, build and continuously optimize deep learning algorithm architectures suitable for financial time series data, high-frequency trading signals, etc. ;
Innovate and improve the evaluation system of deep learning in quantitative trading, including model stability, backtesting effectiveness, and real-time performance;
Push deep learning models from the experimental stage to the actual trading system, participate in model deployment, monitoring and iterative optimization processes;
Track the latest advances in machine learning and deep learning, and apply the latest research results to the development of practical trading strategies.

Work requirements

Computer science, statistics, applied mathematics, artificial intelligence or related professional master's degree or above, with a preference for a doctorate;
In-depth understanding of representative algorithms in various subfields of machine learning and deep learning (such as supervised learning, reinforcement learning, graph neural networks, time series modeling, self-supervised learning, etc.)
Have solid research accumulation in a certain deep learning subfield, have in-depth understanding and practical experience of current state-of-the-art models;
Have published many first-author papers in international top conferences such as NeurIPS, ICML, CVPR, ICCV, ECCV, EMNLP, SIGKDD, IJCAI, AAAI, etc., demonstrating outstanding research and innovation abilities;
Have solid foundations in deep learning theory, while also having practical experience in modeling complex real-world data, and able to achieve cross-domain model transfer and adaptation;
Have complete online deep learning modeling experience, including feature engineering, model training, hyperparameter tuning, performance evaluation, and system integration;
Have strong cross-team collaboration skills, be able to communicate and cooperate efficiently with data engineering, strategy research and system architecture teams.

Statistical Modeling

Machine Learning (ML)

Natural Language Processing (NLP)

Deep Learning

Pattern Recognition

Mandarin