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.
Job Summary:
We are seeking a highly skilled Senior Quantitative Researcher to join our team and drive the development and optimization of trading strategies using advanced AI modeling techniques. In this role, you will collaborate closely with quantitative traders and developers to analyze vast datasets, build predictive models, and enhance strategy performance. Additionally, you will have the opportunity to lead a quant research team, mentoring junior researchers and overseeing high-impact projects. The ideal candidate is a data-driven problem-solver with strong leadership potential, a background in machine learning, statistical analysis, and financial markets, and a passion for applying AI to real-world trading challenges. This position offers significant growth opportunities in a fast-paced, collaborative environment.
Role and Responsibilities:
Team Leadership and Mentorship: Potentially lead a team of quantitative researchers, providing guidance on research direction, project prioritization, and methodology. Mentor junior team members, foster a culture of innovation and feedback, and ensure high standards of research quality and collaboration.
Model Development and Validation: Build and validate complex quantitative models for multi-asset classes (e.g., equities, options, futures, crypto). Apply techniques such as large-scale portfolio optimization, time-series forecasting, and anomaly detection to minimize risks and maximize alpha, with oversight of team contributions.
AI Modeling and Strategy Optimization: Design, implement, and refine AI-driven models (e.g., machine learning algorithms, neural networks, reinforcement learning) to optimize trading strategies, improve predictive accuracy, and enhance risk-adjusted returns. Focus on areas such as signal generation, portfolio optimization, and execution algorithms.
Collaboration with Teams: Work hand-in-hand with quantitative traders to translate research insights into actionable trading strategies, and partner with developers to integrate models into production systems, ensuring seamless deployment and real-time performance monitoring. Oversee cross-functional projects involving multiple stakeholders.
Data Analysis and Research: Conduct rigorous statistical analysis on large-scale financial datasets (market data, alternative data, etc.) to identify patterns, anomalies, and opportunities. Develop and backtest hypotheses using tools like Python, R, or MATLAB, while delegating tasks to team members as appropriate.
Performance Monitoring and Iteration: Monitor live strategy performance, conduct post-trade analysis, and iterate on models based on empirical results. Identify and mitigate issues like overfitting, slippage, or market regime shifts, while leading team reviews and improvements.
Qualifications:
Education: Advanced degree (Master’s or PhD) in Quantitative Finance, Computer Science, Statistics, Mathematics, Physics, Engineering, or a related field.
Experience: 5+ years in quantitative research, preferably in a trading firm, hedge fund, or investment bank, with at least 2 years in a leadership or supervisory role (e.g., mentoring juniors or leading projects).
Proven track record in AI/ML applications for trading strategy optimization.
Experience leading quant research teams in high-frequency trading (HFT) or mid-frequency strategies.
Publications or contributions to open-source projects in AI/quant finance.
Technical Skills:
Proficiency in programming languages such as Python (with libraries like TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy) or C++ for model development and optimization.
Strong expertise in machine learning, statistical modeling, and optimization techniques (e.g., convex optimization, Bayesian methods).
Experience with big data tools (e.g., SQL, Spark) and cloud platforms (e.g., AWS, Google Cloud) for handling large datasets.
Domain Knowledge: Deep understanding of financial markets, trading strategies (e.g., high-frequency, mid-frequency, systematic options), and risk management. Familiarity with quantitative trading concepts like alpha generation, backtesting, and transaction cost analysis.
Leadership and Soft Skills: Demonstrated ability to lead teams, provide constructive feedback, and manage projects effectively. Excellent analytical and problem-solving abilities, with strong communication skills to collaborate across teams and mentor others. Ability to thrive in a high-stakes, fast-paced environment.