Data Scientist

Aeon Credit Service; Kuala Lumpur; On-site; MYR 180,000 - 260,000; Full time

Job summary

Aeon Credit Service in Kuala Lumpur is seeking a Data Scientist to lead and support credit data analytics, credit score modelling and machine learning initiatives to strengthen credit risk management and support data-driven business decisions.

You will collaborate with cross-functional teams to develop, deploy, and monitor models, build data pipelines, maintain data quality, and provide actionable insights for management and credit policy.

Qualifications

• Bachelor's Degree or higher in Data Science, Statistics, Mathematics, Computer Science, Economics, Actuarial Science, Finance, or other relevant disciplines.

Responsibilities

• Lead credit data analytics and modelling initiatives by analysing customer, credit and collection data to identify trends, patterns, risks and opportunities across the credit portfolio.
• Develop, implement and monitor credit scoring models and predictive models to support credit assessment, risk management, collection strategies and overall portfolio performance.
• Apply data science and machine learning techniques to solve business and credit-related problems, including data mining, feature engineering, model development, validation and performance monitoring.
• Translate complex data and analytical findings into actionable business insights and recommendations for management, credit policies and business strategies.
• Develop and maintain data analytics pipelines and solutions, ensuring data quality, accuracy and reliability throughout the data lifecycle.
• Support machine learning model deployment and maintenance (MLOps), including model implementation, monitoring, performance tracking and continuous improvement.
• Analyse consumer and market behaviour and conduct competitor and market benchmarking to identify emerging trends and potential business or credit risks.
• Collaborate with Credit, Risk, IT, Data, Business and other relevant stakeholders to understand business requirements and develop practical, data-driven solutions.
• Lead digital transformation and automation initiatives involving credit analytics, machine learning, predictive modelling and digital solutions.
• Monitor portfolio and model performance and provide regular analysis and reporting to identify areas requiring corrective action or improvement.
• Lead, coach and develop team members, providing technical guidance, knowledge sharing and mentoring to strengthen the team's analytical and modelling capabilities.
• Ensure analytical projects are delivered accurately and within agreed timelines, while maintaining appropriate documentation, governance and quality standards.

Education

Bachelor's Degree or higher in Data Science, Statistics, Mathematics, Computer Science, Economics, Actuarial Science, Finance, or other relevant disciplines.

Tools

SQL

Python

R

Tableau

Power BI

AWS

Job description

Jora Malaysia will close on 9th September 2026. Thank you for being with us, we are cheering you on as you continue your career journey.

As a Data Scientist, you will lead and support credit data analytics, credit score modelling and machine learning initiatives to strengthen credit risk management and support data-driven business decisions.

Your key responsibilities include:

Lead credit data analytics and modelling initiatives by analysing customer, credit and collection data to identify trends, patterns, risks and opportunities across the credit portfolio.

Develop, implement and monitor credit scoring models and predictive models to support credit assessment, risk management, collection strategies and overall portfolio performance.

Apply data science and machine learning techniques to solve business and credit-related problems, including data mining, feature engineering, model development, validation and performance monitoring.

Translate complex data and analytical findings into actionable business insights and recommendations for management, credit policies and business strategies.

Develop and maintain data analytics pipelines and solutions, ensuring data quality, accuracy and reliability throughout the data lifecycle.

Support machine learning model deployment and maintenance (MLOps), including model implementation, monitoring, performance tracking and continuous improvement.

Analyse consumer and market behaviour and conduct competitor and market benchmarking to identify emerging trends and potential business or credit risks.

Collaborate with Credit, Risk, IT, Data, Business and other relevant stakeholders to understand business requirements and develop practical, data-driven solutions.

Lead digital transformation and automation initiatives involving credit analytics, machine learning, predictive modelling and digital solutions.

Monitor portfolio and model performance and provide regular analysis and reporting to identify areas requiring corrective action or improvement.

Lead, coach and develop team members, providing technical guidance, knowledge sharing and mentoring to strengthen the team's analytical and modelling capabilities.

Ensure analytical projects are delivered accurately and within agreed timelines, while maintaining appropriate documentation, governance and quality standards.

Job Responsibilities

• Lead credit data analytics and modelling initiatives by analysing customer, credit and collection data to identify trends, patterns, risks and opportunities across the credit portfolio.
• Develop, implement and monitor credit scoring models and predictive models to support credit assessment, risk management, collection strategies and overall portfolio performance.
• Apply data science and machine learning techniques to solve business and credit-related problems, including data mining, feature engineering, model development, validation and performance monitoring.
• Translate complex data and analytical findings into actionable business insights and recommendations for management, credit policies and business strategies.
• Develop and maintain data analytics pipelines and solutions, ensuring data quality, accuracy and reliability throughout the data lifecycle.
• Support machine learning model deployment and maintenance (MLOps), including model implementation, monitoring, performance tracking and continuous improvement.
• Analyse consumer and market behaviour and conduct competitor and market benchmarking to identify emerging trends and potential business or credit risks.
• Collaborate with Credit, Risk, IT, Data, Business and other relevant stakeholders to understand business requirements and develop practical, data-driven solutions.
• Lead digital transformation and automation initiatives involving credit analytics, machine learning, predictive modelling and digital solutions.
• Monitor portfolio and model performance and provide regular analysis and reporting to identify areas requiring corrective action or improvement.
• Lead, coach and develop team members, providing technical guidance, knowledge sharing and mentoring to strengthen the team's analytical and modelling capabilities.
• Ensure analytical projects are delivered accurately and within agreed timelines, while maintaining appropriate documentation, governance and quality standards.

Job Requirements

• Bachelor's Degree or higher in Data Science, Statistics, Mathematics, Computer Science, Economics, Actuarial Science, Finance, or other relevant disciplines.
• Minimum 8 years of relevant experience in Data Science, Credit Risk Analytics, Credit Modelling, Predictive Modelling or related fields.
• Candidates with experience in banking, financial institutions, credit card, consumer finance or other financial services environments will have an added advantage.
• Minimum 3 years of experience in a supervisory or leadership capacity, with demonstrated experience in managing, mentoring or developing team members.
• Strong experience in analysing credit portfolio, customer behaviour, credit risk or collection data and translating analytical findings into business recommendations.

Technical Skills

• Strong proficiency in SQL for data extraction, manipulation and analysis.
• Proficiency in Python or R for data analysis, statistical modelling and machine learning.
• Practical knowledge of machine learning algorithms, predictive modelling and data mining techniques.
• Good understanding of feature engineering, model development, model validation and performance monitoring.
• Knowledge of credit scoring / scorecard development and validation techniques.
• Understanding of MLOps, model deployment and model maintenance will be an advantage.
• Experience with AWS Cloud Services is preferred; experience with other cloud platforms will also be considered.
• Experience with data visualisation and business intelligence tools such as Tableau, Power BI or similar tools is an advantage.
• Good understanding of credit operations, credit processes and credit policies.