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Credit Risk Model Developer, SrAssc

State Street

State Street

Software Engineering
Hangzhou, Zhejiang, China
Posted on Jan 21, 2026

The Centralized Modeling Analytics & Operations (CMAO) team within Enterprise Risk Management (ERM) of State Street provides support in the development, deployment, ongoing monitoring, and documentation of tools and methods for assessing various aspects of market, credit, operational, and liquidity risk for SSC. The team’s work is focused on building models to ensure compliance with the regulatory requirements including Basel, CCAR (Comprehensive Capital Analysis and Review), CECL, IFRS 9 and ICAAP, by supporting robust risk assessment and governance practices.

The CMAO team operates globally, and this position is based in Hangzhou, China, with a focus on wholesale credit risk modelling and ongoing monitoring. The role will initially involve monitoring model performance, with the opportunity to develop into an independent model developer over time.

What you will be responsible for

As Credit Risk Model Developer you will

  • Support the maintenance and enhancement of the ongoing monitoring framework for wholesale credit risk models by conducting quarterly performance reviews, analyzing results, and preparing reports to validation group
  • Critically review regulatory mandates and develop statistical models to estimate the probability of default and loss given default for wholesale portfolio
  • Perform econometric and statistical analysis including assumption tests and sensitivity analysis, to evaluate and validate model performance
  • Prepare technical documents for internal (model owners, users, Model Validation Group, Internal Audit) and external (External Audit, regulators) audience
  • Collaborate with the implementation team to ensure functional and accurate model deployment by reviewing documentation, coordinating tests, and analyzing test results of all model changes
  • Partner with model owners, users and other business units outside CMAO to ensure that quantitative models truly reflect SSC’s risk profile and business practices
  • Work collaboratively across the three lines of defense to ensure the appropriate modeling parameters and characteristics are captured, reviewed, and challenged
  • Support ad‑hoc projects in risk management, measurement, and related areas

Preferred Qualifications

  • Masters’ or PhD in Economics, Statistics, Mathematics, Risk Management, Finance or related field
  • Strong knowledge of multivariate statistics and time series analysis
  • Proficiency in programming languages such as R, Python, SAS, or SQL
  • Excellent written and verbal communication in English
  • Ability to manage projects and work independently on complex analytical tasks
  • Strong preference given towards experience in credit risk
  • Familiarity with regulatory frameworks (e.g., Basel, CCAR, CECL, IFRS 9, ICAAP) is preferred
  • Experience in banking sector risk management is considered an asset

About State Street

Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success.

We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.

As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.

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