Operational Data Scientist - Predictive Analytics

CACEIS; Putrajaya; Hybrid; MYR 60,000 - 90,000; Full time

Job summary

CACEIS Malaysia is building a predictive analytics capability to proactively manage operational errors across asset servicing. The role sits in a hybrid data professional capacity, consolidating fragmented data, designing and maintaining data pipelines, and developing predictive models linking error patterns to key operational drivers.

You will transform raw data into actionable insights to reduce errors, strengthen operational resilience, and support reporting, dashboards and stakeholder

Qualifications

• 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.

Responsibilities

• 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.
• 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.
• 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.
• 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

Job description

CACEIS Malaysia is building a predictive analytics capability to proactively manage operational errors across asset servicing. The role sits in a hybrid data professional capacity, consolidating fragmented data, designing and maintaining data pipelines, and developing predictive models linking error patterns to key operational drivers.

You will transform raw data into actionable insights to reduce errors, strengthen operational resilience, and support reporting, dashboards and stakeholder