Join the innovative 55ip Quant team and help shape the future of portfolio optimization using advanced quantitative, ML and AI techniques. This is your opportunity to grow your quantitative development skills and make a direct impact on investment technology.

As a Software Engineer II at JPMorgan Chase within the 55ip Quant team, you will collaborate with quants, product and technology professionals to design, implement, and optimize quantitative models for portfolio management. You will contribute to the development of quantitative solutions, working in an agile environment to deliver robust, scalable technology. Your work will directly influence investment strategies and client outcomes.

Job responsibilities

• Develop, implement, and maintain quantitative models for portfolio optimization.
• Develop secure, high-quality production code in Python and review and debug code written by your team.
• Design and integrate models into the simulation environment.
• Improve strategy performance through analytics review and code tuning.
• Apply software engineering best practices across development.
• Identify opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 2+ years applied experience
• Proficiency in Python and advanced experience with numerical libraries (Num Py, pandas, scikit-learn).
• Hands-on practical experience in high performance system design, application development, testing, and operational stability
• Hands-on experience developing, testing, and deploying quantitative, ML/AI models.
• Experience working in an agile development environment.
• Experience with cloud technologies, especially AWS.
• Proven experience working with large datasets.

Preferred qualifications, capabilities, and skills

• Experience working with a quantitative team.
• Knowledge of statistical and quantitative modeling techniques, including experience applying these methods to real-world data analysis and problem-solving
• Prior experience in portfolio construction, implementation or trading technology.