Data Scientist (12-months) M/F

Crédit Agricole Group; Putrajaya; On-site; MYR 60,000 - 90,000; Full time

Job summary

CACEIS Malaysia is building a predictive analytics capability to proactively manage operational errors across its asset servicing activities. A hybrid data professional will consolidate fragmented data, design data pipelines, and develop predictive models linking error patterns to key operational drivers.

The role aims to transform raw data into actionable insights that reduce errors and strengthen resilience.

Qualifications

• Consolidate fragmented, non-standardised operational data.
• Design and maintain structured data pipelines.
• Develop predictive models linking error patterns to operational drivers.
• Transform raw data into actionable insights to reduce errors and strengthen resilience.

Responsibilities

• Data preparation and management of operational data from multiple sources.
• Cleansing and validation to ensure data quality.
• Maintain structured datasets for reporting and analytics.
• Develop and maintain operational reports and dashboards.
• Support ad-hoc reporting requests from stakeholders.
• Collaborate with Operations, Technology and Business teams to understand data requirements.

Skills

Data preparation

Data cleansing

Data analysis

Predictive modelling

Dashboard development

Reporting

Job description

What is the opportunity?

CACEIS Malaysia has been a cornerstone of asset servicing since 2008. Strategically located in IOI City, Putrajaya, the company employs over 1,300 skilled professionals, providing a broad spectrum of services to institutional clients across Europe including Custody & Cash Clearing, Fund Administration, Fund Distribution, Master Data Services, Market Solutions, Group Invoicing, Reconciliations and Private Equity & Real Estate Solutions. As a Centre of excellence, CACEIS Malaysia plays a pivotal role in driving the scalability of global operations, enhancing service quality, and implementing standardized processes to meet the diverse needs of its clients. The dedicated team in Malaysia is committed to delivering reliable, high-quality solutions that empower clients to thrive in a dynamic and competitive financial landscape.

CACEIS Malaysia is building a predictive analytics capability to proactively manage operational errors across its asset servicing activities. This role is the cornerstone of that initiative — a hybrid data professional who will consolidate fragmented, non-standardised operational data, design and maintain structured data pipelines, and develop predictive models that link error patterns to key operational drivers such as team capacity and competency, processing volumes, and platform performance. The successful candidate will transform raw operational data into actionable, forward-looking insights that reduce errors and strengthen operational resilience.

What will you do?

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

Analytics and Predictive Model Support

• Assist in analyzing operational data to identify trends and patterns.
• Support the preparation of datasets for predictive modelling activities.
• Assist in testing, validating and documenting predictive model outputs.
• Perform basic statistical and trend analysis under guidance.

Reporting & Dashboard Development

• Develop and maintain operational reports and dashboards.
• Produce recurring management reports using approved reporting tools.
• Support ad-hoc reporting requests from business stakeholders.
• Highlight trends, anomalies and operational insights

Learning, Collaboration & Continuous Improvement

• Collaborate with Operations, Technology and Business teams to understand data requirements.
• Participate in process improvement and automation initiatives.
• Continuously develop technical knowledge in analytics, data engineering and AI technologies.
• Support documentation of processes and analytical methodologies

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