About Matrixport

Founded in 2019, Matrixport is a global digital asset financial services platform bridging traditional finance and the digital asset ecosystem through technology and regulatory compliance. The company has grown into a unicorn with a valuation exceeding US$1 billion. Matrixport and its subsidiaries maintain a licensed and regulated footprint across major financial centres, including Singapore, Hong Kong, Switzerland, the United Kingdom, and the United States. Matrixport manages over US$7 billion in assets and offers institution-grade services spanning custody, wealth and yield management, structured products, and trading solutions. The firm focuses on building secure, compliant, and scalable digital financial services for global clients.

For more information, visit www.matrixport.com

Why Join Us

At Matrixport, we tackle complex problems as a team. We encourage openness and promote transparency, respect, and inclusivity. Every team member is valued and has a voice that can be heard. We are always in search of intellectually curious and entrepreneurial individuals who are keen on making an impact in the crypto ecosystem and in building a better product for the next one billion users.

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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