Analyst

About the Role

The Structured Finance Analytics team under the Global Banking & Markets division is seeking a motivated professional to support the Credit & Asset Finance business. The team has product expertise across residential assets, consumer loans, real estate, and warehouse financing. The position sits at the core of securitization execution. A successful candidate will architect optimization frameworks and demonstrate an affinity for a solution-oriented mindset.

Key Responsibilities

• Perform large-scale data manipulations across millions of loan-level records delivering portfolio-level insights and strategic asset selection to traders, bankers, and clients using statistical methods.
• Act as a client advisor and perform analytics on all advisory securitization transactions including any MBS classes and consumer ABS classes.
• Collaborate with clients and work with data from various sources including:
• Settlement Data
• Latest Month End Servicer Data
• TPR due diligence Data
• Originator Data
• Perform data validations and create portfolio level stratification and replines.
• Own portfolio collateral analysis across the full deal lifecycle – from asset selection, pricing, marketing, and closing using python, CAS, and SQL.
• Provide pool level CPR speeds, loss severity, and credit enhancement from RA (Fitch, KBRA, Moodys, Milan) models.
• Assist in populating credit memos and PPM materials with outside accountants and lawyers.
• Perform asset pool selection based on contribution requirements on ABS/CMBS securitization.
• Work with rating agencies and create historical performance matrices (CPR, CDR, Charge off, Recoveries, Loss) to project future performance.
• Manage cross-functional relationships with the desk, IBD, transaction management, diligence, operations, controller, and technology teams to ensure monthly portfolio activities are accurately represented.
• Strong communication skills (written and spoken) to translate technical analytics into client-facing insights.

Requirements & Tech Stack

• Python
• CAS
• SQL

Qualifications

• Bachelor’s degree (U.S. or foreign equivalent).
• 0 to 3 years of loans / Fixed income experience or internship in related fields.
• Undergraduate in Finance, Economics, Mathematics, or other STEM related degrees.
• Experience working with large structured and unstructured datasets (millions/billions of loan records).
• Understanding of fixed income analytics (duration, convexity, yield modeling).
• Familiarity with mortgage or consumer credit performance data.
• Ability to operate in high-pressure execution environment with parallel deal flow.