About BIT：
BIT (formerly Matrixport) is a global digital asset financial services and infrastructure group. Headquartered in Singapore and founded in 2019, BIT bridges traditional finance and digital assets through governance-driven financial services and technology.
The firm manages over US$7 billion in assets and facilitates more than US$7 billion in monthly trading volume. BIT offers services including custody, trading, asset and wealth management, liquidity and financing solutions, and tokenised real-world assets (RWA), serving institutional and professional investors globally.
BIT Group entities maintain a licensed and regulated footprint across Singapore, Hong Kong, Switzerland, the United Kingdom, the United States and Bhutan.
For more information, visit www.bit.com
ABOUT THE ROLE
• Assist in the analysis and modeling of financial time series data.
• Participate in medium-to-long-term market macro modeling based on alternative data, such as text data (news, social media) for sentiment analysis and topic mining.
• Model market capital flow dynamics by analyzing market microstructure, using trade and order book data to apply high-frequency data in low-frequency contexts.
• Abstract market logic into trading rules, and test the robustness of the logic and the stability of the alpha.
• Implement and test basic machine learning models, and optimize various model components from multiple dimensions based on task objectives.
• Perform logical attribution analysis on model performance under different market environments, and iterate the model based on real-time trading feedback.
Requirements
• Educational Background: Master’s degree candidate in Computer Science, Statistics, Mathematics, or a related field.
• Programming and Data Processing Skills:
o Understand the mathematical principles of time series analysis and be able to implement common processing and feature engineering methods.
o Possess experience in text data processing and be familiar with the basic process and tools for sentiment analysis.
• Mathematical and Statistical Fundamentals:
o Familiar with common probability distributions, statistical inference, and hypothesis testing methods, and understand the application of Fourier and Laplace transforms from a probabilistic perspective.
o Familiar with basic optimization theory, stochastic processes, and fundamentals of differential equations.
o Master the principles and application scenarios of core machine learning algorithms.
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