Quantitative Developer/Engineer
Listed on 2026-03-06
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IT/Tech
AI Engineer, Data Science Manager, Data Scientist, Data Analyst
Location: New York
A leading Multi Manager, fresh off an exceptional 2025 performance, is actively expanding and seeing an experienced Quantitative Developer/Software Engineer to join its high-performing team in New York. This is an exclusive opportunity to work alongside some of the brightest, highest-pedigree investment professionals in the industry and contribute directly to a top performing team.
In this role, you'll work closely with the Head of the team, building and supporting production grade quantitative software across analytics, risk and portfolio management. It's an extremely hands-on position with full ownership throughout the development lifecycle, ideally suited for someone who wants to take on leadership responsibilities while remaining deeply involved in core engineering work.
Responsibilities:- Design, develop and maintain quantitative libraries, analytics and data pipelines supporting valuation, risk, performance and scenario-based analysis.
- Build and integrate end-user tools, APIs, and scalable services that expose quant models across firm-wide systems
- Apply strong engineering practices - testing, version control, monitoring, CI/CD - while providing production support and contributing to ongoing platform evolution.
- Collaborate with quant researchers, risk teams and developers to translate requirements into robust, maintainable software
- 4-7 Years of experience in a quantitative development, software engineering or financial analytics in a production environment
- Advanced degree in a STEM discipline from a top school
- Deep experience with C++ and Python and delivering production quality software for analytics, risk, pricing or trading systems
- Familiarity with scenario-based analysis (backtesting, simulation, and version control)
- Experience with Java
- Experience in a front office, buy side, or quantitative technology environment
- Statistical or machine learning technologies applied to financial or time series data
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