Quantitative Software Engineer (Quantitative Investment)
Job Description
Job Title: Quantitative Software Engineer (Quantitative Investment)
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Job Location: Morristown, NJ
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Work Model: Hybrid (Onsite 3x/week)
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Job Type: Permanent / Full-Time
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Mode of Interview: Video / Technical Interview (Onsite Final)
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Must Have Skills: Strong Python/Julia, C/C++, Data Repository Architecture (SQL/Oracle), Math/Statistics aptitude, MS/PhD degree
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Position Summary:
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This position plays a key role in contributing to core quantitative investment processes. Quantitative Software Engineers are responsible for the design, implementation, optimization, and testing of proprietary quantitative investment systems. Working as part of a collaborative team, the role involves building high-performance systems, managing data infrastructure, and enhancing portfolio models.
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Key Responsibilities:
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- Quantitative Modeling & Optimization: Implement and enhance quantitative models, proprietary portfolio construction, and optimization techniques in close collaboration with Research and Portfolio Management teams.
- Data Architecture: Architect and manage global data repositories incorporating millions of financial and market data points.
- High-Performance Computing: Create high-performance simulation and optimization engines.
- Workflow Systems: Develop and manage end-to-end investment workflow management systems.
- Analytics & Reporting: Build and extend analytics and reporting platforms.
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Requirements & Qualifications:
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Education:
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- MS or PhD degree in Computer Science, Engineering, Statistics, or a related quantitative discipline.
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Core Programming:
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- Strong programming proficiency in Python or Julia (experience with NumPy/Pandas or similar quantitative software is a plus).
- Hands-on programming experience in C/C++.
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Database Management:
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- 2+ years of experience with relational database technologies (e.g., SQL Server, Oracle).
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Technical & Analytical Aptitude:
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- Outstanding coding, debugging, and analytical skills paired with a strong aptitude for mathematics and statistics.
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Problem Solving:
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- Passionate about solving complex business, data, and technical challenges.
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Knowledge of .NET and C#.
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Active contributions to open-source software.
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