Algo Trading Quant
## Key Responsibilities
Strategy Design & Research
Design and implement predictive quantitative trading models, including both market-making and directional strategies, for automated execution on decentralized perpetual exchanges such as Hyperliquid
Apply statistical modeling and research methodology - including regression analysis, time-series forecasting, and machine learning - to identify and validate trading signals across varying time horizons
Lead research efforts to discover new alpha sources, optimize strategy parameters through rigorous backtesting, and expand the number of available strategies to support growing user capacity
Perform experiment design, dataset generation, feature engineering, and model building on financial datasets, following the full research-to-production pipeline
Run simulations and estimate market impact of quantitative models, accounting for realistic assumptions around slippage, fees, funding rates, and liquidity constraints
Proactively identify trading opportunities and risks by analyzing historical market data across multiple markets and instruments
## Required Qualifications
3+ years of professional experience developing and deploying quantitative trading models at a top-tier quantitative trading firm (e.g., Jane Street, Wintermute, Jump Trading, Citadel, Two Sigma, DRW, Tower Research, HRT, or equivalent). Crypto algo trading experience is beneficial but not required
Proven track record of building and deploying profitable automated trading strategies in production environments, with real P&L attribution
Experience with machine learning applied to trading: feature engineering, model selection, hyperparameter tuning, overfitting mitigation, and deployment of models into production
Strong understanding of statistical modeling, time-series analysis, regression techniques, and quantitative research methodologies
Solid software engineering practices: version control (Git), automated testing, CI/CD pipelines, and writing production-quality code that others can maintain and extend
Experience with full-cycle development: researching, prototyping, writing and debugging code, deploying to production, and monitoring and maintaining live trading systems
• *If you’re passionate about blockchain and decentralized technologies, explore more opportunities in web3 and cryptocurrency careers.**
Apply tot his job
Apply To this Job
Strategy Design & Research
Design and implement predictive quantitative trading models, including both market-making and directional strategies, for automated execution on decentralized perpetual exchanges such as Hyperliquid
Apply statistical modeling and research methodology - including regression analysis, time-series forecasting, and machine learning - to identify and validate trading signals across varying time horizons
Lead research efforts to discover new alpha sources, optimize strategy parameters through rigorous backtesting, and expand the number of available strategies to support growing user capacity
Perform experiment design, dataset generation, feature engineering, and model building on financial datasets, following the full research-to-production pipeline
Run simulations and estimate market impact of quantitative models, accounting for realistic assumptions around slippage, fees, funding rates, and liquidity constraints
Proactively identify trading opportunities and risks by analyzing historical market data across multiple markets and instruments
## Required Qualifications
3+ years of professional experience developing and deploying quantitative trading models at a top-tier quantitative trading firm (e.g., Jane Street, Wintermute, Jump Trading, Citadel, Two Sigma, DRW, Tower Research, HRT, or equivalent). Crypto algo trading experience is beneficial but not required
Proven track record of building and deploying profitable automated trading strategies in production environments, with real P&L attribution
Experience with machine learning applied to trading: feature engineering, model selection, hyperparameter tuning, overfitting mitigation, and deployment of models into production
Strong understanding of statistical modeling, time-series analysis, regression techniques, and quantitative research methodologies
Solid software engineering practices: version control (Git), automated testing, CI/CD pipelines, and writing production-quality code that others can maintain and extend
Experience with full-cycle development: researching, prototyping, writing and debugging code, deploying to production, and monitoring and maintaining live trading systems
• *If you’re passionate about blockchain and decentralized technologies, explore more opportunities in web3 and cryptocurrency careers.**
Apply tot his job
Apply To this Job