Credit Risk Data Scientist

About Us
We are living in dynamic times. Technology is reshaping how we live, and we want to use it to redefine how financial services are offered. This is why we are coming together to unlock big dreams, and financial inclusion for people in our region is just one of them. We want to build a digital bank with the right foundation - using data, technology and trust to solve problems and serve customers. 

We're seeking a driven and skilled Credit Risk Data Scientist to join our dynamic team. If you're passionate about predictive modelling, risk management, and leveraging cutting-edge technologies, this role offers an exciting opportunity to contribute in two key areas:

Credit Risk Models:

• Develop, maintain, and monitor predictive models for credit scoring, fraud detection, and suspicious activity prediction.

• Write high-quality, production-ready code for models to be deployed in the production environment.

• Deploy models in the MLOps production environment, maintaining and troubleshooting any issues.

• Support regular model performance reviews, audits, and recalibrations.

• Leverage alternative data sources to enhance underwriting and credit risk models.

• Ensure alignment with model governance, business, and regulatory expectations.
   

Data Analytics to Help Business:

• Collect, process, and analyze data to support statistical analysis and risk decision-making.

• Design, implement, and maintain performance reports and actionable insights dashboards.

• Provide data-driven insights and develop dashboards to support loan approval processes and business decision-making.

• Conduct in-depth analyses to identify trends, patterns, and anomalies in lending data that can influence credit risk strategies.

• Present model concepts and metrics to senior stakeholders and validators/auditors.
   

Experience and Qualifications

• Degree (PhD, Master's, Bachelor's) in Statistics, Mathematics, Economics, Finance, Risk Management, or related field.

• Proven experience building risk models such as Application Score, Behavior Score, PD, EAD, LGD (Basel or IFRS 9), especially for retail customers in diverse countries including Singapore.

• Comprehensive understanding of risk management throughout the customer journey within fintech or risk-focused environments.

• Domain expertise in retail credit products, banking, and regulatory frameworks (Basel, IFRS).

• Proficiency in Python, R, SQL, Snowflake and Data Visualization tools like Tableau.

• Technical proficiency in data retrieval, data modeling, data mining, and predictive modeling.

• Strong familiarity with advanced machine learning techniques, statistical classification/regression for credit risk.

• Exceptional problem-solving skills, detail-oriented mindset, and ability to thrive in a dynamic work setting.

• Bonus: Familiarity with implementing models using Terraform scripts, Docker/Kubernetes, CI/CD technologies, AWS, or MLOps.