Responsibilities:

📍Research and develop quantitative trading strategies using NLU methods—sentiment analysis, intent recognition, named-entity extraction—on financial news, social media, and other text sources 📍Design and build machine-learning models to uncover predictive trading signals and perform exploratory data analysis on large, complex datasets 📍Apply mathematical techniques (probability, statistics, time-series analysis) to refine and strengthen trading models 📍Rigorously backtest strategies against historical data and iteratively optimize models to boost performance and curb risk

Requirements:

📍Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, Financial Engineering or a related discipline 📍Solid grasp of NLU techniques, including sentiment analysis, intent recognition, and named-entity recognition 📍Proficiency in Python or R, with hands-on experience in NLP libraries (SpaCy, NLTK, Transformers) 📍Strong mathematical foundation—probability, statistics, linear algebra, time-series analysis—and familiarity with ML frameworks (Scikit-learn, TensorFlow, PyTorch) 📍Excellent analytical and problem-solving abilities, with strong communication and teamwork skills 📍Bilingual English/Mandarin is required to be able to coordinate with overseas partners and stakeholders.

If you’re passionate about blockchain and decentralized technologies, explore more opportunities in web3 and cryptocurrency careers.