Franklin Resources
Quantitative Developer
Pasadena, California, United States of America
Sponsorship not specified$140k-$170kDetected 22 days ago
PythonSQLLinuxData Engineering
About the role
- We leverage cutting edge strategies and deep insights to unlock opportunities for long term wealth creation.
- Our talented, global teams bring expertise that is both broad and unique.
- The role requires working directly with business stakeholders and familiarity with the calculation of risk and analytics for fixed income instruments.
Responsibilities
- Design, develop, and maintain production-grade fixed income risk solutions used by the Investment Solutions team for customized client needs.
- Collaborate with technology teams to deliver maintainable and production ready software solutions.
- Support the business teams as needed to ensure the full potential of systems and data is realized.
Requirements
- Minimum 3 years of experience in a technical role within a quantitative finance or investment management environment
- Strong programming skills, with proficiency in Python, SQL and Linux
- Experience working with large financial datasets
- Degree in either Computer Science, Mathematics, Engineering or other quantitative discipline.
Nice to have
- Understanding of portfolio risk and scenario analysis
- Experience working with Aladdin.
- Additional post graduate qualifications such as MFE or CFA.
- Franklin Templeton is an Equal Opportunity Employer.
Compensation
- Along with base compensation, employees are eligible for an annual discretionary bonus, a 401(k) plan with a generous match, and recognition rewards.
Benefits
- Three weeks of PTO in your first year
- Competitive medical, dental, and vision insurance to support your well-being
- Employee Stock Investment Plan (ESIP) with discounted share purchase opportunities
Company info
- To support your ongoing growth and career advancement
Equal opportunity
- Equal Opportunity Employer.
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This listing is sourced directly from Franklin Resources's careers page and normalized into a canonical job model.