Senior Associate Model Implementation And Data Management

Job description / Role

Role purpose

The role requires very strong technical and communication skills. Manage risk data including data capturing, organizing, storing, and analyzing for development, validation, and implementation of risk rating models and scorecards. Conduct risk analytics and generate comprehensive risk reports. Provide advanced quantitative analytics support to the overall risk management function. Formulate management techniques for quality data collection to ensure adequacy, accuracy, and legitimacy of data. Engage in risk architecture projects including implementation of various risk IT applications, data analytics, data flows, standards, and processes. Strong knowledge of information technology systems, risk management systems, tools, applications, and relational database management systems. Software development and ability to code. Develop and implement AI analytics to analyze risk data. Use machine learning techniques to identify patterns, trends, and potential risks including exploratory data analysis and data quality and trend assessment. Design data tracking and monitoring tools. Analyze and validate data, ensuring data security. Ensure all data management and AI analytics practices comply with relevant regulations and organizational policies.

Key accountabilities / responsibilities

• Manage and maintain data for risk models implementation and scorecards development.
• Oversee credit risk management processes, including Basel II and IFRS 9 compliance.
• Conduct risk analytics and generate comprehensive risk reports.
• Utilize statistical tools such as SAS, R, and Python for data analysis and model development.
• Write advanced SQL queries to extract, manipulate, and analyze data.
• Develop and implement data science, machine learning, and artificial intelligence solutions to enhance risk management processes.
• Apply techniques such as generative AI, classification, regression, clustering, and other related methods.
• Collaborate with cross-functional teams to ensure data integrity and accuracy.
• Communicate findings and insights effectively to stakeholders.
• Foster a collaborative and team-oriented work environment.
• Stay updated with industry trends and advancements in AI and data analytics.
• Continuously seek opportunities to enhance risk management processes.

Education and experience

Minimum 3 years of total experience in handling data management projects, data science, machine learning, model development within the banking and finance sector preferably in the credit risk domain, i.e., Basel II and IFRS 9, risk models development and implementation, risk analytics, reporting. Bachelor's degree in computer science, engineering, information systems, or a related field. Master's degree is preferred.

Specialist skills / technical knowledge required for this role

• Experience working with large and complex data sets, including alternative data (bureau, open banking, etc.) for credit models.
• Experience in credit risk modelling and risk analytics preferred.
• Experience with data science, machine learning, and artificial intelligence techniques, including generative AI, classification, regression, and clustering.
• Possess strong quantitative skills and solid experience in developing, validating, and monitoring risk models.
• Knowledge of the credit scoring systems available in the market and their use.
• Advanced user of statistical software such as SAS and R or Python and SQL.
• Good knowledge of handling risk technologies and their implementation.
• Ability to work independently on multiple tasks and/or projects.
• Excellent oral and written communication skills in English.
• Proficiency in risk concepts, banking products, operations, systems, and pertinent regulatory requirements.
• Flexible team player and able to work and deliver under pressure.