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# Quantitative Desk Strategist - Fixed Income - Associate

- **Company:** Morgan Stanley
- **Location:** New York, NY, United States
- **Salary:** $150,000.0 - $200,000.0
- **Contract:** Temporary contract
- **Skills:** Java (Programming Language), Artificial Intelligence, C++ (Programming Language), Databases, Distributed Systems, Python (Programming Language), Logistic Regression, Machine Learning, Information Technology, Software Coding
- **Published:** May 31, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=cdb1d7e63ad6da7d

## About the Role

Do you have experience in Software coding?, * Master's or Ph.D. in Mathematics, Physics, Engineering, Operations Research, Computer Science, Statistics, or similar quantitative fields.

 * Strong coding skills; willingness to learn Scala quickly is required. Prior Scala or Python experience is a strong plus.
 * Solid foundation in probability, statistics, numerical methods, and data modeling.
 * Ability to communicate complex ideas clearly using data, mathematics, and practical examples.
 * Curiosity about markets and willingness to learn bonds, ETFs, futures, and derivatives quickly.
 * Comfort having your ideas challenged and challenging others constructively.
 * Strong ownership, practical judgment, and attention to detail.
 * A genuinely good sense of humor.

Nice to Have

 * AI-assisted coding experience.
 * Experience with kdb/q or other high-performance time-series databases.
 * Experience with Java, C++, or distributed systems.
 * Familiarity with machine learning techniques such as logistic regression, random forests, clustering, or classification models.
 * Prior exposure to fixed income, electronic trading, market microstructure, or risk management.
 * Experience building large-scale, transaction-processing, or real-time decision systems.

## Description

* Develop quantitative models and analytics for pricing, risk, inventory, market impact, and trading performance.
 * Build reliable tools and workflows used directly by traders and strategists.
 * Analyze large, noisy, real-time datasets to identify patterns, risks, and opportunities.
 * Explain model results, assumptions, limitations, and trade-offs to both technical and non-technical audiences.
 * Partner closely with traders to improve decision-making across bonds, futures, ETFs, derivatives, and related products.
 * Investigate P&L drivers and distinguish repeatable edge from temporary market conditions.
 * Contribute to scalable systems that connect trading decisions across instruments, products, and regions.


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