Lead Software Engineer- Python / Quant

As a Lead Software Engineer- Python / Quant Development / Quant Research at JPMorganChase within the Asset and Wealth Management Technology Team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

We are looking for developers to contribute to the transformation of the Quant Research Platform and Portfolio Construction and Management stack into an industry-leading platform. If you are an expert in Python, business-focused, and have a strong grounding in statistical methods and data analytics, please apply. You will work alongside Quant Research on model development, validation workflows, and the build-out of reusable Python Quant libraries that standardize research-to-production patterns and accelerate delivery across the platform.

You’ll be required to apply your depth of knowledge and expertise across the software development lifecycle, partnering continuously with Quant Research on a daily basis.

Job Responsibilities

Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems

Develops secure and high-quality production code, and reviews and debugs code written by others

Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.

Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems

Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and 5+ years applied experience

Hands-on practical experience delivering system design, application development, testing, and operational stability

Strong experience with Python coding

Experience in financial Services across Asset Classes

Knowledge of statistical methods, quant or data analytics

Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.

Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices

Proficient in all aspects of the Software Development Life Cycle

Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security

In-depth knowledge of the financial services industry and their IT systems

Practical cloud native experience

Preferred qualifications, capabilities, and skills

CFA charter holder

Experience with systematic portfolio management

Experience with model development, validation workflows, and the build-out of reusable Python Quant libraries