Highlight of the engagement opportunity:

- Nature of role: Full time

- Number of years of experience expected: - (2 - 7) years.

- Educational qualification expected: Graduate/ Postgraduate/ Actuarial Science

- Areas of past experience preferred: Credit risk modeling, IFRS 9 PD / LGD/ EAD modeling, Retail

analytics, market risk, ALM modeling

- Preferred geography of previous work experience: India / Europe / APAC / US

- Application experience: Python, R, C++, MSSQL, VBA

Key responsibility areas:

- Work directly and manage BFSI/ Corporate clients across multiple geographies

- Preparation of reports, presentations and model validation summaries and present to client senior management

- Develop and validate quantitative models across several areas such as quantitative & actuarial modelling, ALM, financial risk management (credit, market and liquidity risks), treasury, FTP, budgeting and planning, climate risk, capital adequacy, stress testing.\\

- Develop end-to-end functionalities on PD, LGD, EAD, IFRS 9 / credit risk models and on financial instrument valuations, risk assessment, scenario analysis and simulations.

- Develop end to end functionalities for modelling macro-economic factors and market prices like Interest rate, Credit Spread, FX, Commodities and Volatility.

Selection process:

We seek to be transparent during the selection process. While the actual process may vary from the process indicated below, the key steps involved are as follows:

- Interview: There are expected to be at least 2 rounds of interviews. The number of interview rounds may increase depending on the criticality and seniority of the role involved.

- Final discussion on career and compensation: Post final selection, a separate discussion will be set up to discuss compensation and career growth. You are encouraged to seek any clarifications.

Preparation required:

It is recommended that you prepare on the following aspects before the selection process:

- Demonstrate proficiency in financial instrument valuation and risk assessment

- Demonstrate proficiency in statistical, econometrics and time series analysis.

- Demonstrate proficiency in use of back-end server-side languages like Python/C++/Java

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