Equity Volatility Quantitative Researcher

About Balyasny Asset Management L.P.

Established in 2001, Balyasny Asset Management L.P. (BAM) is a premier institutional investment firm known for generating steady, uncorrelated absolute returns across all market conditions. With offices spanning Chicago, New York, Greenwich, San Francisco, Hong Kong, London, and Singapore, BAM prides itself on attracting top-tier talent through a rigorous selection process. The firm fosters a vibrant work environment to motivate employees daily, supplying them with the resources needed to excel and unlock their full potential.

Role Overview

BAM is recruiting a seasoned Quantitative Researcher skilled in developing, maintaining, and integrating globally accessible quantitative trading infrastructure. This role involves close collaboration with portfolio managers and quantitative researchers to develop tailored toolkits for trading analytics. The ideal applicant will bring substantial experience from a financial services setting, robust technical expertise, and comprehensive knowledge of quant trading platforms including back testing, simulations, performance evaluation, and market data management. This position demands strong communication skills, multitasking capability, and adaptability to a fast-paced trading atmosphere.

Primary Responsibilities

• Provide support to portfolio managers and analysts by building customized alpha research tools utilizing proprietary analytics.
• Contribute to the enhancement of internal analytics platforms targeting D1 and Equity Derivative products.
• Collaborate with investment teams to design valuation models and screening tools aimed at optimizing trading and filtering decisions.
• Conduct testing on various trading strategies, engage in ad hoc research, and present findings using Excel and Python environments.
• Assist trading and risk departments with scenario analyses, relative value assessments, and basis trading analytics.
• Coordinate with business users and platform developers to gather requirements and facilitate integration of vendor models and datasets.
• Document key model assumptions, code infrastructure, and user-facing APIs effectively.
• Operate independently with prudent judgment.
• Identify opportunities to automate manual tasks to improve efficiency.
• Perform unit testing, performance benchmarking, and code profiling to ensure numerical accuracy and stability.

Required Qualifications

• Master's degree or PhD in quantitative fields such as Mathematics, Physics, Statistics, Engineering, Computational Finance, or related disciplines.
• Proficient programming skills in Python for idea testing and research infrastructure development; familiarity with C++ is advantageous.
• Understanding of listed and OTC markets for D1 and equity options, alongside volatility indices; experience with local or stochastic volatility models highly desirable.
• Solid knowledge of statistical methods including time series analysis and regression techniques.
• Experience in building trading tools and familiarity with alpha research and signal generation are preferred.

Ideal Candidate Attributes

• Strong collaborative mindset and eagerness to work effectively within a team environment.
• Excellent problem-solving capabilities and aptitude to devise and apply appropriate solutions.
• Ability to prioritize and juggle multiple projects and tasks efficiently to meet deadlines.
• Competence in composing clear, concise documentation to communicate ideas, requirements, and issues.
• Keen attention to detail coupled with superior organizational skills.