中国平安
Risk Talent
Risk Talent
发布于 大约 16 小时前普通员工/个人贡献者
香港
中级经验
全职员工
仅现场办公
本科
机器学习
大数据
风险管理
模型部署
金融科技
信用风险
AI 估算 · 30k–60k
香港金融科技岗位，机器学习技能稀缺，薪资水平较高且具有竞争力。
职位详情
关于这个职位
This role focuses on building machine learning models for credit risk control using big data, including prediction of default and loss of contact. You'll work with IT and business teams on model deployment, monitoring, and strategy formulation. Ideal for those skilled in Python and interested in fintech risk analytics.
最低要求
University degree in Computer Science, Information Systems or related field; Familiar with Python programming
工作职责
Establish machine learning models in credit risk control business based on big data, and establish prediction models for default, loss of contact and other behaviors
Assist IT team and business team in model deployment, monitoring and adjustment
Assist in formulating strategies for pre-loan review, post-loan monitoring and other situations
Assist in formulating and filling out external and internal credit risk control reports
优先资格
Experience in big data programming is preferred
AI 洞察
优缺点分析
- Work on cutting-edge machine learning applications in a critical area (credit risk) with access to vast financial data.
- Join a leading global financial conglomerate (Ping An) with strong brand and resources for career growth.
- Gain exposure to end-to-end model lifecycle from development to deployment and monitoring.
- Located in Hong Kong, a global financial hub with high salary and diverse opportunities.
- Requires strong technical skills and the ability to translate business needs into model solutions under tight deadlines.
- May involve high pressure due to the sensitive nature of credit risk and regulatory compliance.
- Competition among talent in Hong Kong's fintech scene is intense, requiring continuous learning.
- This role is ideal for data scientists or ML engineers with a solid Python background who are passionate about applying AI to financial risk management.
角色解读
- Advance to senior risk modeler or lead data scientist within the risk analytics team, taking on more complex modeling projects.
- Move into risk strategy or management roles, overseeing credit risk frameworks for the company's lending products.
- Transition to broader fintech roles such as quantitative analyst or AI engineer in financial services.
- Design and develop machine learning models to predict credit risk behaviors like default and loss of contact using big data.
- Collaborate with IT and business teams to deploy, monitor, and fine-tune models in production.
- Help create risk strategies for loan pre-review and post-loan monitoring, and prepare internal/external risk reports.
- Strong proficiency in Python programming is essential for model building and data manipulation.
- Understanding of big data technologies (e.g., Spark, Hadoop) is preferred to handle large-scale data.
- Knowledge of statistical modeling and machine learning algorithms (e.g., regression, decision trees, neural networks) for credit risk.
申请策略
- Tailor your cover letter to express interest in fintech and risk analytics, mentioning Ping An's innovative culture.
- Research Ping An's risk management projects or AI initiatives to show genuine interest and knowledge.
- Emphasize projects where you built predictive models for financial or similar tabular data, highlighting model performance metrics.
- Showcase experience with big data tools (e.g., Spark, PySpark) and deployment frameworks (e.g., Docker, Kubernetes).
- List any domain knowledge in credit risk, banking, or fintech, even if from coursework or personal projects.
- Quantify impact (e.g., reduced default rate by X%) to demonstrate business value.
- Deepen Python skills with libraries like scikit-learn, XGBoost, and TensorFlow/PyTorch for machine learning.
- Learn big data processing with Spark or Hadoop to stand out for the preferred qualification.
面试指南
- Use STAR method (Situation, Task, Action, Result) to structure answers with concrete examples.
- Emphasize the end-to-end process: data understanding, feature engineering, model selection, evaluation, and deployment.
- Show awareness of business constraints and explain trade-offs between model complexity and interpretability.
- Explain how you would build a credit risk prediction model using large-scale customer data.
- Describe your experience with model deployment and monitoring in a production environment.
- How do you handle imbalanced datasets in default prediction?
- Walk us through a machine learning project from data collection to business impact.
- What validation techniques do you use to ensure model stability and fairness?
匹配度报告
68
综合匹配度
Top-tier fintech, cutting-edge ML skills, competitive pay in HK, but on-site with no flexibility.
适合人群
This role is best suited for candidates prioritizing technical growth and compensation over work-life balance.
最强匹配
成长发展匹配
最弱匹配
工作生活匹配
薪资福利75
成长发展85
工作生活50
使命价值60
薪资福利匹配
75中等
The role is with a top-tier financial group, offering competitive compensation in Hong Kong. However, exact salary is not disclosed, and benefits are unknown.
薪资信号未披露 (30K-60K/月)
成长发展匹配
85较高
The position involves cutting-edge machine learning and big data technologies, offering strong skill development. No explicit promotion path mentioned, but company size suggests growth opportunities.
技术前沿前沿/新兴技术
技术栈Python、机器学习、大数据、信用风险
业务类型profit_center
工作生活匹配
50较低
Work location is specified as Hong Kong on-site, with no mention of remote or flexible hours. Work-life balance signals are absent.
工作模式仅现场办公
办公地点未明确
加班情况未提及（无法判断）
使命价值匹配
60中等
Working in credit risk control contributes to financial stability and responsible lending, providing a sense of purpose. The fintech industry is growing but not a high-social-impact field.
行业发展高速增长赛道
社会影响中性/一般
创新程度积极采用新技术
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