Risk Instruments 信用风险工具 ID206508

Objective of Job
• Lead the development, implementation, validation, monitoring, and periodic enhancement of credit risk scoring models, including application and collection scorecards.
• Design, develop and maintain fraud-detection scorecards, rule sets, and analytics to identify and prevent application and behavioral fraud across channels.
• Drive adoption and responsible use of advanced technologies (e.g., machine learning, AI, and feature rich data pipelines), ensuring explainability, robustness and compliance with model governance.
• Develop and operationalize portfolio-level scoring tools and frameworks (e.g., retention, balloon refinance scoring, decision matrices, business policy rules, and procedures).
• Ensure stable, scalable and secure operation of scoring engines, model deployment pipelines and monitoring infrastructure (including automated performance alerts and recalibration workflows).
• Collaborate closely with HQ, local IT, cloud teams and third party vendors to deliver localized model implementations, cloud/on prem deployment and integration projects.
• Partner with internal business units to define requirements, support new initiatives and translate business needs into data driven analytics and controls.
• Lead exploration, sourcing, validation and stewardship of third party and alternative data (including vendor evaluation, data quality checks and privacy/compliance considerations).
• Manage external vendors and stakeholders to ensure high quality, timely deliveries and adherence to SLAs and governance standards.
• Promote technical best practices (feature engineering, model interpretability, A/B testing, CI/CD for ML) and drive continuous improvement of the anti fraud and credit risk ecosystem.
Job Description
Responsible for development of scoring models and business policy rules utilized in support of retail credit, collections, retention and remarketing
• Ensure development and implementation of anti-fraud scoring models & tools, decision matrix and business rule policies
• Ensure development and implementation of application scoring models & tools, decision matrix and business rule policies
• Ensure development and implementation of portfolio scoring models & tools, decision matrix and business rule policies
• Monitor performance of scoring engine/tools and lead updates & changes if necessary
• Work together with Retail Credit, Collections, Retention and Remarketing teams to understand business needs and provide strong scoring supports
Manage operations and maintenance of the scoring engine and ensure transparency and governance for correct scorecard and business policy rules (BPR) operations
• Own the local credit risk scoring process & documents
• Ensure timely and accurate maintenance of scoring engines & tools and BPRs with respect to effectiveness and efficiency
• Coordinate the UAT, hot-fix and change request processes in close collaboration with local IT and related business units
• Liaise with Sales, Operations and Credit Operations to provide scoring & analytical supports to business development & digital transformation
• Be responsible for monitoring and validation of scorecards & BPRs
Drive innovation of scoring models and BPRs development
• Introduce and promote new technologies and algorithms
• Work closely with the Global RI to ensure development and implementation of scoring models and business rule policies
• Monitor and validate performance of scorecards and BPRs
• Support system implementation and changes
• Work together with Credit Operations and Collections to understand business needs and provide strong statistic analysis supports
Monitor and implement regulatory requirements, in particular NAFR and PBOC
• Support close monitoring and follow up on changes in the legal/regulatory environment based on own research and input from the various functions of the organization, in particular Legal and Regulatory Compliance
• Implement regulatory requirements relevant to the areas of application and portfolio scoring
• Ensure and enhance measures on consumer right protection
Miscellaneous tasks
• Undertake new tasks of the team
• Support other team members for team tasks
• Draft process/instruction of major tasks for the purpose of better documentation
• Share knowledge with team members and other colleagues
Qualification
• Education & Experience: Bachelor’s or Master’s degree in Statistics, Mathematics, Computer Science or related fields, or equivalent experience;
• 5+ years in credit risk or fraud risk modeling (banks or auto finance companies preferred).
• Technical Modeling Expertise: Extensive hands on experience developing and validating credit scorecards and fraud detection models using logistic regression and advanced ML (GBM, XGBoost/LightGBM, deep learning).
• End to End ML Production: Proven experience with feature engineering, model training, evaluation, deployment and production monitoring; strong emphasis on model explainability and robustness.
• ML Ops & Automation: Practical experience implementing containerized model deployment, automated monitoring and retraining pipelines, and version control for code/model/feature assets.
• Data & Engineering Skills: Proficient in Python and advanced SQL; experience with big data tooling (Spark/Databricks) and handling large, heterogeneous datasets.
• Governance & Compliance: Solid understanding of model governance and data privacy requirements.
• Cross functional Leadership: Experience leading cross team projects, managing external vendors, and translating technical outputs into operational rules and business decisions.
• Domain Knowledge: Practical knowledge of auto finance products and fraud typologies is strongly preferred.
• Communication: Strong written and verbal English for collaboration with HQ and external partners.
Preferred (but not mandatory)
• Cloud & Infrastructure: Familiarity with cloud or private cloud deployments and integration of scoring engines into production environments.