Global Banking & Markets - Private - Structured Finance Trade Analytics - Analyst - Dallas

SRI Conference; Dallas (TX); Presencial; USD 70.000 - 100.000; Jornada completa

Descripción de la vacante

SRI Conference's Structured Finance Analytics team within Global Banking & Markets seeks a motivated professional to support the Credit & Asset Finance business. The role involves architecting optimization frameworks and delivering analytics across securitization, including asset selection and deal lifecycle insights.

You will work with large loan-level datasets, provide client-facing analytics for advisory securitization transactions, and collaborate with cross-functional teams to ensure

Formación

• Bachelor's degree or foreign equivalent in a related field.
• Experience with Python, SQL and reporting tools required.
• Experience handling large structured and unstructured datasets.
• Understanding of fixed income analytics is a plus.

Responsabilidades

• Perform large-scale data manipulations across millions of loan-level records delivering portfolio-level insights.
• Act as a client advisor and perform analytics on advisory securitization transactions including MBS and consumer ABS classes.
• Own portfolio collateral analysis across full deal lifecycle using Python, CAS, and SQL.
• Provide pool level CPR speeds, loss severity and credit enhancement from RA models.
• Assist in populate credit memo and PPM materials with outside accountants and lawyers.

Conocimientos

Python

SQL

Reporting tools

Educación

Bachelor's degree in Finance, Economics, Mathematics, or STEM

Descripción del empleo

TheStructured Finance Analytics team under 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. The successful candidate will be responsible for the following:

JOB DUTIES:

• 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. Responsibilities include:
  • 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 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 populate credit memo 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 relationship with the desk, IBD, transaction management, diligence, operation, controller, and technology team to ensure monthly portfolio activities are accurately represented.
• Strong communication skills (written and spoken) to translate technical analytics into client-facing insights.

MINIMUM EDUCATION REQUIREMENTS/DEGREE AND FIELD:

Bachelor's degree (U.S. or foreign equivalent)

MINIMUM YEARS EXPERIENCE REQUIRED:

0 to 3 years of loans / Fixed income experience or internship in related fields

Preferred Skillsets:

• Undergraduate in Finance, Economics, Mathematics, or other STEM related degrees.
• Experience with python, SQL and reporting tools required.
• 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.