Amber Group is a global leading digital asset company providing crypto financial services to both institutional and high-net-worth investors globally. 

We offer best-in-class liquidity solutions and cutting-edge trading infrastructure across major exchanges, applications, and networks. With over $1 trillion in cumulative trading volume, our deep liquidity helps power the digital asset ecosystem. 

Beyond trading, our full-suite of offerings includes wealth management, lending and investing products. But at our core, we focus on building strong relationships and delivering personalized service to help clients navigate this fast-growing industry.

At Amber, security is our #1 priority. We have invested years of effort and millions of dollars in cybersecurity, crypto-security, and operational security across the firm, with industry-leading certifications like SOC 2 Type II and ISO 27001.

Powered by a 400+ team of traders, technologists and engineers operating 24/7 globally, our technology and research capabilities are world-class. Yet we remain entrepreneurial, always seeking fresh ideas and risks worth taking. We are always interested in people who have an appetite for taking calculated risk, demonstrate a high level of original thinking and intellectual curiosity.   

Role and Responsibilities:

• Conceptualize, develop, and refine quantitative models used for crypto trading by leveraging real-time data analysis.
• Perform back-testing of quantitative strategies to ensure efficacy and efficiency.
• Conduct statistical analysis and implement machine learning techniques to predict market movements and identify trading signals.
• Work collaboratively with the developers to implement algorithms into the trading platform.
  

Qualifications:

• MS or PhD in Computer Science, Statistics, Mathematics, or a related field.
• At least one year of proven experience as a quantitative researcher within a dynamic systematic trading environment.
• Demonstrated ability to conduct independent research utilizing large datasets.
• Strong programming skills in Python, or similar languages used in quantitative finance.
• Experience with machine learning libraries and frameworks such as TensorFlow, PyTorch.
• Ability to work in a fast-paced and collaborative environment.
• Solid knowledge of secondary financial markets such as the Hong Kong stock, U.S. stock or Crypto exchanges, with previous investment experience highly preferred.