Key Responsibilities:
Design and develop quantitative trading strategies for cryptocurrencies using statistical, machine learning, and econometric methods.
Analyze high-frequency crypto market data, including order book dynamics, on-chain metrics, and alternative datasets, to uncover trading opportunities.
Create predictive models for price movements, volatility, liquidity, and other crypto-specific market dynamics.
Rigorously backtest trading strategies using historical and simulated crypto market data to assess performance and robustness.
Work closely with quantitative developers to implement research models into production-ready code for real-time trading.
Stay informed on cryptocurrency market trends, blockchain protocols, DeFi ecosystems, and regulatory developments to inform strategy design.
Continuously refine models and strategies to adapt to the unique volatility and liquidity characteristics of crypto markets.
Integrate risk controls, including wallet security and counterparty risk, into models to comply with internal and regulatory standards.
Document research methodologies, model performance, and findings clearly for team review and audit purposes.
Qualifications:
Master’s or PhD in a quantitative field such as Mathematics, Statistics, Financial Engineering, Computer Science, Physics, or a related discipline.
2+ years of experience in quantitative research, ideally in cryptocurrency trading, high-frequency trading, or related financial markets.
Demonstrated success in developing trading strategies or predictive models, preferably for digital assets.
Advanced proficiency in Python, R, or MATLAB for data analysis and modeling.
Familiarity with C++ or other languages for collaboration with developers is a plus.
Expertise in statistical modeling, time-series analysis, and machine learning techniques.
Experience with crypto-specific data (e.g., order book data, on-chain analytics, DEX/CEX APIs).
Deep understanding of cryptocurrency markets, blockchain technology, DeFi protocols, and market microstructure (e.g., AMMs, staking, liquidity pools).
Strong problem-solving skills with the ability to derive insights from noisy and complex datasets.
Ability to articulate complex quantitative concepts to both technical and non-technical team members.
Bonus Skills:
Experience with alternative data sources (e.g., social sentiment, on-chain transaction data).
Knowledge of smart contract analysis or blockchain forensics.
Familiarity with cloud platforms (e.g., AWS, GCP) or distributed computing for large-scale data processing.
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