Data Analyst_MAS Regulatory Reporting

Data Analyst – Corporate Banking

Role Overview:

We are seeking a highly analytical and detail-oriented Data Analyst to be part of us. The role will focus

on analysing large datasets, generating actionable insights, and supporting strategic decision-making.

The ideal candidate will have strong data management skills, financial domain knowledge (Preferably

Regulatory Reporting, Risk and Compliance in a corporate banking products), and the ability to

communicate findings in a clear, business-oriented manner.

Key Responsibilities

• Collect, clean, and validate data from multiple banking systems (e.g., loan management,

treasury, trade finance, payments).

• Perform quantitative and qualitative analysis on corporate banking portfolios, including

lending, deposits, trade finance, and treasury products.

• Work with stakeholders to identify business needs and translate them into data-driven

solutions.

• Conduct trend, variance, and scenario analysis to support pricing, profitability, and client

segmentation.

• Ensure compliance with data governance, quality, and regulatory reporting standards.

• Partner with technology teams to improve data pipelines, automate reporting, and

implement advanced analytics use cases.

Required Skills & Qualifications

• Bachelor’s degree in data science, Statistics, Economics, Finance, Computer Science, or a

related field.

• Minimum 8 years of experience as a Data Analyst, ideally within banking or financial

services.

• Familiarity with statistical analysis tools(Python, R) preferred.

• Good understanding of corporate banking products (loans, deposits, trade finance,

treasury, payments).

• Knowledge of risk, compliance, and regulatory reporting frameworks in banking.

• Strong analytical, problem-solving, and critical-thinking skills.

• Excellent communication skills to translate technical data into business insights.

Preferred Attributes

• Experience in data warehousing, ETL, or big data platforms (Ex: Snowflake, Hadoop,

Spark).

• Exposure to machine learning techniques for credit/risk scoring or client segmentation.

• Strong business acumen with an ability to connect data insights to revenue, cost, and risk

drivers.

• Ability to work in cross-functional teams within a fast-paced banking environment