Description :

Tech Data Analyst Regulatory Reporting/Credit Risk

Location : Pune/Bangalore/Hyderabad Work from Office

Other Tier 1/Tier 2 Cities Work from Home may be considered

Experience : 5 to 12 Years

About the Role :

A Large Global Company is looking for experienced Business Data Analysts with strong expertise in Traded Risk, Market Risk, Credit Risk, and Counterparty Credit Risk domains.

The candidate should have hands-on experience with Python or SAS, strong SQL skills, and experience working with large banking datasets and Interest Rate Risk (IRR) regulatory programmes.

Key Responsibilities :

- Perform data analysis for credit risk and regulatory reporting initiatives.

- Use Python, SAS, and SQL for data extraction, validation, reconciliation, and analysis.

- Support IRR (Interest Rate Risk) regulatory programme requirements. (Must have)

- Work with datasets related to EAD, PD, LGD, CRR, RWA, CARM, limits, products, and securities. (Must have)

- Identify data quality issues and perform root cause analysis.

- Prepare validation reports, dashboards, reconciliations, and documentation.

- Collaborate with business, risk, and technology stakeholders for reporting and analysis requirements.

- Support automation and optimization of regulatory reporting processes.

Required Skills :

- 5 to 12 years of Data Analysis experience within banking, financial services, risk, or regulatory reporting domains.

- Strong understanding of Credit Risk concepts such as EAD, PD, LGD, CRR, and RWA. (MUST HAVE)

- Good understanding of CARM, limits, banking products, and securities. (MUST HAVE)

- Hands-on experience with Python or SAS for data analysis. (Must have)

- Strong SQL knowledge and experience handling large-scale banking datasets.

- Experience working with BI/Analytical tools such as Qlik.

- Advanced MS Office skills, especially Excel and reporting tools.

- Strong analytical, data validation, and problem-solving skills.

- Prior experience with global banking clients or regulatory reporting automation is preferred.