Business Analyst, Fraud Analytics & Financial Crime, Group Data & Analytics, Group Strategy & I[...]

Maybank; Kuala Lumpur; On-site; MYR 60,000 - 90,000; Full time

Job summary

A leading financial institution in Kuala Lumpur is seeking a data analyst to optimize data sets for financial crime compliance use cases. Responsibilities include analyzing complex datasets, developing visualizations, and collaborating with stakeholders. A Bachelor's degree in relevant fields and proficiency in SQL and data science tools are required. Strong analytical and problem-solving skills are essential for this role.

Qualifications

• Strong proficiency in SQL and MS Excel.
• Exposure to financial crime compliance analytics (AML, fraud detection, KYC).
• Solid foundation in applied statistics and regression methods.

Responsibilities

• Extract, script, and optimize data sets for FCC use cases.
• Perform in-depth analysis of complex datasets.
• Develop and enhance data visualization modules and dashboards.

Skills

SQL

Data visualization

Statistical analysis

Machine learning algorithms

Problem-solving

Communication

Education

Bachelor’s degree in Actuarial Science, Computing, Mathematics, Physics, Engineering or related disciplines

Tools

SAS

R

Python/NumPy

MatLab

Hadoop

GIS tools (e.g., MapInfo)

Job description

Key Responsibilities

• Extract, script, and optimize data sets to enhance productivity and effectiveness in FCC use cases.
• Perform in-depth analysis of complex datasets using statistical and visualization techniques.
• Explore, study, and integrate data across internal systems and external public sources.
• Develop, expand, and enhance in-house visualization modules and dashboards.
• Identify key trends, correlations, and patterns to generate actionable intelligence.
• Translate FCC concepts into data-driven rules and prototype analytics solutions (metrics, models, outputs).
• Collaborate with stakeholders and stay updated on industry advancements in analytics and data science.

Requirements

• Bachelor’s degree or higher in Actuarial Science, Computing, Mathematics, Physics, Engineering, or related disciplines.
• Strong proficiency in SQL, MS Excel, SAS, and experience with data science tools (R, Python/NumPy, MatLab) and big data frameworks (e.g., Hadoop).
• Solid foundation in applied statistics, regression methods, hypothesis testing, and machine learning algorithms (k-NN, Naïve Bayes, Decision Forest, etc.).
• Proven experience in mathematical model construction, dashboard development, and data visualization; exposure to CUBE/HyperCube methodologies preferred.
• Knowledge of GIS tools (e.g., MapInfo) and thematic data organization; experience in geo-marketing concepts is an advantage.
• Prior exposure to financial crime compliance analytics (AML, fraud detection, KYC, surveillance) strongly preferred.
• Personal qualities: integrity with strict adherence to data privacy, strong logical and critical thinking, problem-solving and communication skills, detail-oriented mindset, willingness to learn/unlearn, and ability to collaborate effectively.