Compliance Data Analyst

Job Title: Data Analyst – Compliance

Location: Singapore (Onsite)

Employment Type: 12-Month Contract

Eligibility: Singapore Citizens and Singapore Permanent Residents (PR) only

An established banking client is looking for a highly analytical Data Analyst – Compliance to join its Financial Crime Compliance team.

The successful candidate will leverage advanced analytics, machine learning, and statistical modelling to strengthen the bank's AML and financial crime detection capabilities.

Key Responsibilities

1. Data Analysis & Business Analytics

1.1Extract, cleanse, transform, and analyse large volumes of structured and unstructured data from multiple internal and external sources.

1.2 Perform exploratory data analysis to identify trends, anomalies, and hidden patterns related to financial crime risks.

1.3Translate complex business and regulatory requirements into structured analytical problems and deliver evidence-based insights.

1.4 Develop dashboards, reports, and data visualisations to support compliance monitoring and decision-making.

2. Machine Learning & Advanced Analytics

2.1Design, develop, validate, and deploy machine learning models to support financial crime detection and compliance monitoring.

2.2Apply advanced analytical techniques including:

Logistic Regression

Random Forest

Gradient Boosting

Neural Networks

Graph Analytics

2.3Evaluate model performance using appropriate statistical and machine learning metrics.

2.4Collaborate closely with Data Engineering and MLOps teams

Requirements:

Qualifications

Bachelor's or Master's Degree in Data Science, Statistics, Mathematics, Computer Science, Financial Engineering

3–7 years of hands-on experience in Data Analytics and/or Data Science, preferably within the Banking or Financial Services industry.

Demonstrated experience delivering descriptive analytics (reporting, dashboards, KPI monitoring) as well as predictive and prescriptive analytics using machine learning.

Exposure to Financial Crime Compliance, including Anti-Money Laundering (AML), Know Your Customer (KYC), Fraud Detection, Sanctions Screening, or Trade Surveillance.

Technical Skills

Advanced proficiency in SQL for data extraction, transformation, and optimisation.

Strong programming skills in Python for data analysis, statistical modelling, and machine learning.

Solid understanding of the complete Machine Learning lifecycle, including feature engineering, model development, validation, deployment, monitoring, and optimisation.

Strong foundation in statistics, including hypothesis testing, sampling techniques, probability distributions, regression analysis, and statistical inference.

Good understanding of Financial Crime Compliance concepts, including AML, Counter-Terrorism Financing (CTF), sanctions compliance, and fraud risk management.

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