Credit Suisse - ENO - Credit Risk Analytics

Credit Suisse

As one of the world's leading banks, Credit Suisse provides its clients with investment banking, private banking and asset management services worldwide. Founded in 1856, Credit Suisse has a long tradition of meeting the complex financial needs of a wide range of clients, offering advisory services, comprehensive solutions and innovative products to companies, institutional clients and high-net-worth private clients globally, as well as retail clients in Switzerland. Credit Suisse is active in over 50 countries and employs approximately 40,000 people. Further information can be found at www.credit-suisse.com.

Cultural diversity is essential to our success. As such, we employ people from more than 100 countries. Credit Suisse empowers employees to work openly and respectfully with each other and with clients, ultimately striving to deliver superior results while offering initiatives and programs to assist employees achieve a healthy work-life balance

We offer

As a member of Credit Analytics, you would be working in one of the following areas:

- Quant India - Develop and maintain models required for estimation of obligor- s: Probability of default (PD), Loss given default (LGD) and Exposure to default (EAD).

- Trade Analysis - Provide credit exposure analysis on pre-and live-trades across all markets (FX, Rates, Equities, Credit) and financial products (Loans, Derivatives) to Trader and credit officers. The exposure is used to determine Credit charges and to assess the impact of new trades on regulatory capital.

- Portfolio Analysis and Controls -

Back-test IMM credit exposure models, works on the enhancement and maintenance of these models and tactical tools;

Credit scenarios stress testing including stress testing methodology, new scenario definitions and regulatory reporting.

- IRC Methodology - Assess issuer risk on the trading book, develop, implement and maintain IRC models.

- Exposure and Collateral Analytics - Develop and calibrate Risk Factor Estimation (RFE) models in Monte Carlo Engine (a statistical technique to calculate exposure) for all asset classes.

- VaR Methodology - Develop and improve - inputs, models and controls for collateralized exposure (EPE, PE) calculations (in INSIGHT)

You Offer

Work performed within the CRM - Credit Analytics team is technical and the candidate is required to have a sound mathematical background with interest in Quantitative finance.

Candidates must be able to demonstrate the following qualifications and competencies:

- Good MS Access/SQL and VBA knowledge; knowledge of basic programming ( C/C++, R, Matlab)

- Analytical and confident personality with excellent relationship skills in order to interact with senior stakeholders.

- Ability to multi-task and prioritize work load on a daily basis, adapting to changing workloads.

- Excellent written and interpersonal communication skills

- Knowledge of Financial products and risk methodologies would be preferred.

Pleas note:

Applicable for :

Mumbai Candidates

Top Tier college