Data Scientist – Predictive Analytics (12-Month)

CACEIS; Hinoba-an; On-site; PHP 1,804,000 - 2,706,000; Full time

Job summary

CACEIS Malaysia is building a predictive analytics capability to proactively manage operational errors across its asset servicing activities. This hybrid data role consolidates fragmented data, designs pipelines, and develops predictive models linking error patterns to drivers such as team capacity and platform performance.

The role focuses on data consolidation, model support, and producing actionable insights to reduce errors and strengthen operational resilience.

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.
• Relevant certifications (e.g. Microsoft Power BI, Azure Fundamentals, Azure Data Engineer/Data Scientist, Google Data Analytics) are preferred but not mandatory.

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.

Skills

Analytical thinking

Communication skills

Time management

Cross-functional collaboration

Education

Bachelor degree in data science / CS / IT / statistics / math / eng / finance analytics or related

Tools

SQL

Power BI

Tableau

Job description

CACEIS Malaysia is building a predictive analytics capability to proactively manage operational errors across its asset servicing activities. This hybrid data role consolidates fragmented data, designs pipelines, and develops predictive models linking error patterns to drivers such as team capacity and platform performance.

The role focuses on data consolidation, model support, and producing actionable insights to reduce errors and strengthen operational resilience.