Data Analyst

5 days ago

Singapore Opensource Full time $80,000 - $120,000 per year

Job Title: 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 2–5 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.

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