Senior Quantitative Analyst

Stanford Black Limited; New York (NY); On-site; USD 150,000 - 200,000; Full time

Job summary

A leading financial services company is looking for a Credit Quantitative Analyst to join its Global Multi-Strategy Hedge Fund team in New York. The role involves developing analytics and signal-generation models to enhance trading strategies. The ideal candidate will have a strong technical background and experience in credit products, working in a collaborative environment to drive innovation in financial analytics.

Qualifications

• Advanced degree in relevant technical fields.
• Experience in quantitative research or model-driven trading.
• Ability to work in a fast-paced, collaborative environment.

Responsibilities

• Research and develop alpha signals for credit and fixed-income instruments.
• Collaborate with portfolio managers to deploy systematic trading strategies.
• Build tools for liquidity monitoring and trade selection.

Skills

Python

Quantitative research

Valuation models

Signal generation

Communication

Education

PhD or Master’s in Mathematics, Physics, Statistics, Engineering, Computer Science

Tools

NumPy

Pandas

Scikit-learn

PyTorch

Job description

Direct message the job poster from Stanford Black Limited

Credit Quantitative Analyst– Global Multi-Strategy Hedge Fund (New York)

One of the most technically advanced buy-side platforms is expanding its central Credit research team in Manhattan. Sitting at the crossroads of discretionary and systematic trading, you’ll build next-generation analytics and signal-generation models that power a live, tick-by-tick credit-pricing engine used by multiple portfolio managers across corporate, sovereign, and convertible bond markets.

What you’ll do:

• Research & Develop alpha signals and valuation models for credit, high-yield, and convertible bonds using Python (NumPy/Pandas/Scikit/PyTorch).
• Own analytics libraries that stream real-time prices, update risk and P&L intraday, and feed directly into trading desks.
• Prototype & back-test systematic credit strategies, working hand-in-hand with PMs to take ideas from whiteboard to production.
• Build screeners & toolkits to improve trade selection, relative-value analysis and liquidity monitoring.
• Collaborate with data engineers and quant devs to integrate alternative datasets and optimise performance across a low-latency stack.

Qualifications:

• Advanced degree (PhD or Master’s) in Mathematics, Physics, Statistics, Engineering, Computer Science or similar.
• Solid experience in quantitative research, desk strat, or model-driven trading—preferably within Credit or Fixed-Income.
• Expert Python developer; familiar with modern software practices and version control. C++ exposure a plus.
• Solid grasp of credit products (cash bonds, CDS, convertibles) and fundamental/relative-value drivers.
• Experience with signal generation, back-testing frameworks, or pricing/analytics libraries in a real-time environment.
• Clear communicator who thrives in a fast-moving, highly collaborative, low-ego culture.

Why apply?

• High impact: your models plug directly into multi-billion-dollar trading books—see your research turn into P&L within days, not months.
• Cutting-edge tech: modern Python-first stack, cloud-native compute, GPU clusters, and a real-time bond-pricing engine.
• Flat structure: work side-by-side with senior PMs while shaping the future of systematic credit at the firm.
• Growth: choose to remain cross-asset or align with a specific desk as the platform scales; every hire is tailored to their strengths.

Please contact [e-mail] for more information.

If this role isn't right for you, but you know of someone who might be interested, we have a market-leading referral scheme in place to thank anyone who refers a friend who is successfully placed! T&Cs apply.

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Finance

• Industries

  Financial Services, Investment Management, and Investment Banking

Referrals increase your chances of interviewing at Stanford Black Limited by 2x

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