Python Quantitative Analyst

POSITION SUMMARY

We are seeking a highly motivated Quantitative Developer with 5–8 years of experience in designing and implementing quantitative analytics solutions within asset management, capital markets, or banking environments. The role involves translating financial research and investment strategies into robust, scalable, and production-ready systems. The ideal candidate combines strong quantitative understanding with solid software engineering capabilities. They should be comfortable working closely with portfolio managers, researchers, and technology teams to build reliable model-driven applications that support portfolio construction, risk analysis, and investment decision-making.

Familiarity with modern development practices and AI-assisted coding tools is expected to enhance productivity and code quality

REQUIRED TECHNICAL SKILLS &

KEY RESPONSIBILITIES

• 5–8 years of experience in quantitative development, analytics engineering, or backend software development within financial services.

• Strong proficiency in Python for building scalable analytics systems and quantitative applications.

Experience with libraries such as Num Py, Pandas, Sci Py, and relevant optimization or statistical packages.

• Solid understanding of quantitative finance concepts such as portfolio optimization, factor models, risk analytics, fixed income analytics, or derivatives pricing.

• Experience translating research models into production systems with emphasis on configurability, modularity, reproducibility, and performance optimization.

• Backend Development:
Design and maintain scalable services and APIs to expose model outputs, portfolio analytics, and risk metrics. Strong understanding of RESTful architecture and microservices principles.

• Data Engineering & Pipelines:
Experience building efficient data pipelines for large-scale financial datasets, ensuring data integrity, performance, and reliability.

• Database & Cloud Exposure:
Working knowledge of SQL-based databases (e.g., Oracle, Snowflake) and cloud platforms (e.g., AWS or similar environments).

• Visualization & Reporting:
Ability to develop analytical dashboards and reporting tools in Python based frameworks technologies to communicate model insights effectively.

• Software Engineering Best Practices:
Strong command over Git-based version control, automated testing, code reviews, CI/CD pipelines, and documentation standards.

• Production Support & Monitoring:
Experience monitoring analytics workflows, diagnosing production issues, and ensuring stability of model-driven systems in live environments.

• AI Tools Familiarity:
Familiarity with AI-assisted development tools (e.g., Git Hub Copilot or similar GenAI tools) to enhance development efficiency, improve documentation, and streamline workflows.

• Stakeholder

Collaboration:

Strong communication skills with ability to gather requirements, explain technical concepts to non-technical stakeholders, and collaborate effectively with cross-functional teams.

• Ownership & Problem-Solving:
Demonstrated ability to independently drive projects from concept to deployment, proactively identify risks, and implement robust solutions

EDUCATION & EXPERIENCE

• Bachelor’s or Master’s degree in Mathematics, Computer Science, Engineering, Finance, or a related quantitative discipline

• Professional certifications in quantitative finance or financial engineering are a plus.