Data Analyst (Corporate Banking) Job at OPENSOURCE PTE. LTD. in Town of Islip

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.