Data Scientist - Predictive Analytics (12m)

Crédit Agricole SA; Hinoba-an; On-site; PHP 1,353,000 - 1,954,000; Full time

Benefits offered by this job

Total rewards program with performance

Flexible benefits

World-class training program

Job summary

CACEIS Malaysia, a Centre of excellence for Crédit Agricole, is building a predictive analytics capability to reduce operational errors and strengthen resilience.

The role focuses on consolidating fragmented data, designing data pipelines, and developing predictive models linking error patterns to operational drivers, with collaboration across Operations, Technology and Finance to deliver data-driven insights.

Qualifications

• Bachelor’s degree in data science, Computer Science, Information Technology, Statistics, Mathematics, Engineering, Finance Analytics or related disciplines.
• Knowledge and practical experience with SQL, Python or Power BI through academic projects is advantageous.
• Certifications (e.g. Microsoft Power BI, Azure Fundamentals, Azure Data Engineer/Data Scientist, Google Data Analytics) are preferred but not mandatory.

Responsibilities

• Data Preparation & Management: Collect, consolidate and prepare operational data from multiple sources.
• Perform data cleansing and validation to ensure data quality and consistency.
• Support the maintenance of structured datasets for reporting and analytics.
• Maintain documentation of data sources and data definitions.

Skills

SQL

Python

Power BI

Data Visualization

Statistical Analysis

Data Modelling

Education

Bachelor’s degree in data science, Computer Science, Information Technology, Statistics, Mathematics, Engineering, Finance Analytics or related disciplines

Job description

CACEIS Malaysia, a Centre of excellence for Crédit Agricole, is building a predictive analytics capability to reduce operational errors and strengthen resilience.

The role focuses on consolidating fragmented data, designing data pipelines, and developing predictive models linking error patterns to operational drivers, with collaboration across Operations, Technology and Finance to deliver data-driven insights.