Quantitative Researcher

Apply quantitative analysis and modeling techniques within the financial services sector, including time-series analysis, linear regression models, and machine learning algorithms for forecasting and risk modeling. Design and implement production-grade research infrastructure in Python, utilizing numerical algorithms and distributed computing systems to enable massively parallel execution of quantitative models across historical and global datasets. Convert quantitative models into profitable systematic trading strategies that automate trading decisions based on return forecasts, incorporating risk management and transaction costs. Monitor post-deployment performance of trading strategies. Refine models through analyses, including performance attribution, factor risk decomposition, robustness testing, and overfitting diagnostics, while accounting for market fluctuations and production feedback. Conduct predictive risk analysis to identify potential adverse market. Work with quantitative research; alpha research in mid-frequency US equities statistical arbitrage strategies; developing technical alphas; and, programming in Python. Requirements: Masters degree in Mathematics, Statistics, Computer Science, Machine Learning, Financial Engineering, or a related field of study, plus two (2) years of experience with quantitative research; alpha research in mid-frequency US equities statistical arbitrage strategies; developing technical alphas; and, programming in Python. Email resume to [email protected] or mail resume to Hannah Ogren, Balyasny Asset Management, L.P., 444 West Lake Street, 50th Floor, Chicago, IL 60606. Must Ref# AL42BAMNY. No phone calls.