Quantitative Developer - Trading Strategy Implementation & Market Risk Factor Research - New York

[Recruiter-Jim]

I am recruiting for the following opportunity in New York City.

Quantitative Developer - Trading Strategy Implementation & Market Risk Factor Research - New York

This is exceptional opportunity to join a leading proprietary trading firm in New York City as a Quant Developer within their Central Risk Team. This is a unique role that offers a rare combination of technical challenge, direct impact on the firm's profitability, and deep engagement with sophisticated trading strategies and quantitative risk management.

Here's what makes this role stand out:

• Bridging Strategy and Execution: You'll be the crucial link between the firm's research team (who are developing high sharpe strategies) and their trading operations, taking cutting-edge strategies and translating them into robust, production-ready code. This includes optimizing for performance, ensuring seamless integration with trading systems, and maintaining high code quality.
• Integrating Strategies into Risk Management: You'll play a key role in embedding these trading strategies within the firm's proprietary risk management framework. This involves collaborating with risk analysts to understand the unique risk factors of each strategy, implementing appropriate monitoring tools, and ensuring compliance with firm-wide risk limits.
• Researching Proprietary Risk Factors: You'll contribute to the firm's research efforts by investigating and developing new proprietary risk factors, potentially specific to individual strategies. This will involve analyzing market data, identifying potential risks, and developing quantitative models to measure and manage those risks.
• Risk Management: Calculating risk measures, pricing risk, and optimizing portfolio allocations often involve solving complex problems with overlapping subproblems. Dynamic programming can significantly improve the efficiency of these calculations.
• Collaborating with Quantitative Researchers: You'll work closely with the firm's quantitative researchers, not only implementing their trading strategies but also actively participating in the code review process. This includes debugging and refactoring strategy code to enhance performance, improve readability, and ensure maintainability. Your expertise in software engineering best practices will be crucial in ensuring the quality and robustness of the firm's trading algorithms.

The Ideal Candidate:

We are seeking a highly motivated and talented Quant Developer with a demonstrably strong work ethic and a genuine passion for financial markets. This individual will possess a rare combination of technical expertise, quantitative acumen, and a collaborative spirit.

Key Qualifications and Attributes:

• Proven Experience and Stability: 2-4 years of experience as a Quant Developer in a top-tier financial institution (e.g., Goldman Sachs, Morgan Stanley, J.P. Morgan) with direct exposure to trading or market risk management. This role is ideally suited for someone who values stability and has demonstrated commitment to their employer, preferably with experience in only one previous role.
• Exceptional Academic Background: Strong academic credentials, including a degree in Computer Science and a Master's in Financial Engineering from a leading university. A high GPA (top 5%) further demonstrates a history of superior academic performance.
• Python Expertise: Exceptional programming skills in Python, with deep knowledge of core language concepts (object model, memory management, GIL) and expertise in libraries like NumPy and Pandas. A strong emphasis on code optimization and performance is crucial.
• Quantitative Foundation: A solid understanding of essential quantitative techniques, including:
  • Algorithms and Data Structures: Proficiency in designing and implementing efficient algorithms, with a strong grasp of fundamental data structures.
  • Principal Component Analysis (PCA): Knowledge of PCA and its application in dimensionality reduction, feature extraction, and identifying key risk factors.
  • Risk Factor Models: Familiarity with various risk factor models (e.g., Barra, Fama-French) and their use in quantifying and managing portfolio risk, including the ability to analyze model outputs, identify limitations, and contribute to the development of proprietary risk factors.
• High-Performance Computing: Proficiency in parallel computing and high-performance computing techniques (e.g., Dask, Vectorization) for efficient execution and analysis of large datasets.
• Financial Markets Acumen: Experience with leveraged risk management and quantitative trading strategies, demonstrating a practical understanding of financial markets and risk.
• Growth Mindset: A "rising star" with high potential who is already exceeding expectations, taking initiative, and demonstrating leadership qualities. A strong desire to transition from a bank to a dynamic hedge fund environment and contribute to a leading firm in the mid-frequency trading space.

Target comp range: $300k-$450k.