Manager Digital Risk

Job Description:

• Design, develop, and implement sophisticated machine learning models for credit risk assessment, utilizing diverse data sources. Continuously refine and optimize credit scoring models to improve predictive accuracy and portfolio performance. Evaluate and implement new ML algorithms and techniques to enhance credit risk modeling capabilities.
• Conduct in-depth quantitative analysis of nano loan portfolio data to identify risk trends and patterns. Develop and maintain risk measurement frameworks and metrics, and utilize statistical modeling and data mining techniques to assess and quantify various risk factors.
• Develop and implement ML-based fraud detection systems to identify and prevent fraudulent activities. Analyze transactional data and behavioral patterns to detect anomalies and suspicious activities. Stay abreast of emerging fraud trends and techniques in the digital lending space.
• Collaborate with data engineering teams to ensure the availability and quality of data for risk modeling. Develop and maintain data pipelines and infrastructure for risk analytics. Ensure compliance with data privacy and security regulations.
• Develop and present comprehensive risk reports and dashboards to senior management. Communicate complex quantitative concepts and findings to non-technical stakeholders. Provide insights and recommendations based on data-driven analysis.
• Evaluate and implement new technologies and tools to enhance risk management capabilities. Stay up-to-date on the latest advancements in machine learning, artificial intelligence, and digital lending technologies.

Job Specifications:

• Min 5-7 years of experience in developing and implementing machine learning models for credit risk assessment.
• Minimum Bachelor's degree in Quantitative Finance, Statistics, Computer Science, or a related field (Master's Preferred).

Knowledge/Skills:

• Strong proficiency in programming languages such as Python or R.
• Deep understanding of statistical modelling and data mining techniques.
• Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and big data technologies.
• Strong knowledge of financial regulations and risk management principles.
• Excellent analytical, problem-solving, and communication skills.
• experience with database query languages such as SQL.
• Machine learning (e.g., neural networks, random forests, gradient boosting)
• Statistical modelling and data analysis
• Credit risk modelling and scoring
• Fraud detection and prevention
• Data management and infrastructure
• Programming (Python, R, SQL)

Last date to apply is 14th July 2026.