Risk Instruments 信用风险工具

• 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.

• 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

• 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

• 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

• 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

• 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

• 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.

• Cloud & Infrastructure: Familiarity with cloud or private cloud deployments and integration of scoring engines into production environments.

Mercedes-Benz