REQUIRED SKILLS

• Extensive hands-on experience in bond pricing
• Proven expertise in developing risk engines
• Advanced proficiency in Python
• Strong knowledge of fixed income cash products

POSITION OVERVIEW

We are looking for a Senior Quantitative Analytics Lead with deep expertise in Fixed Income and advanced Python development. This individual will spearhead the buildout of a next-generation risk engine to support comprehensive market risk management—primarily across Fixed Income cash instruments.
This is a high-impact role offering the opportunity to contribute meaningfully to a critical transformation effort in a fast-paced, elite team setting.

QUALIFICATIONS

• 10–15+ years of experience in quantitative modeling, data science, and financial software development
• Proven track record leading Fixed Income-centric quantitative initiatives
• Expertise in designing and managing complex risk models, including evaluating model trade-offs
• Strong knowledge of risk frameworks and financial regulations
• Advanced Python programming skills for model building, statistical analysis, and production-level coding
• Full-stack development capability
• Excellent analytical, problem-solving, and organizational skills
• Strong communication abilities and collaborative mindset for working across business and technology teams
• Advanced degree in Financial Engineering, Mathematics, Statistics, or a related technical field
• Experience with AWS is considered a plus

• Lead the design and development of a high-performance quantitative library tailored for Fixed Income cash products, including:
• Investment-grade and high-yield corporate bonds
• Fixed income ETFs
• Sovereign and emerging market (EM) bonds
• Loans, distressed debt, CoCos, preferred stocks, and trade claims
• Bond futures and bond future options
• Enable core features of the next-gen market risk platform, such as:
• Pricing
• Curve stripping
• Full-revaluation Value at Risk (VaR)
• Sensitivities and stress testing
• Architect and implement:
• Issuer-specific and sector curve cohorting methodologies
• A robust risk factor mapping waterfall for issuer/proxy curve selection
• Ensure the risk engine meets business goals and complies with all applicable regulatory requirements
• Oversee the full model development lifecycle—from initial design and coding (primarily in Python), through testing, deployment, validation, and performance monitoring
• Provide analytical support during model releases and lead comprehensive testing and quality assurance efforts
• Maintain and expand Python-based quantitative libraries used in production, with a focus on accuracy, performance, and scalability
• Mentor junior team members and promote a culture of high standards, innovation, and ongoing learning
• Keep up with evolving market trends, technologies, and regulatory guidelines to maintain best-in-class modeling standards