Responsibilities:

📍Research and develop quantitative trading strategies using NLU methods such as 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 optimise 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 📍Strong mathematical foundation: probability, statistics, linear algebra, time-series analysis and familiarity with ML frameworks (Scikit-learn, TensorFlow, PyTorch) 📍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) 📍A passion for exploring undefined problem space in the fast changing crypto world

The crypto industry is evolving rapidly, offering new opportunities in blockchain, web3, and remote crypto roles — don’t miss your chance to be part of it.