Crisil - Senior Analyst - Data Analytics - Fixed Income

The Fixed Income Senior Analyst is responsible for supporting the investment portfolio reporting and quantitative analyses for an Global Asset Manager's Solutions Group. The analyst will assist with the development, calibration, prototyping and documentation of risk models, as well as the preparation of materials for client and senior management. The analyst will also assist with the development of a robust risk management framework and analytics for BIS portfolios.

Responsibilities:

- Support investment portfolio reporting and quantitative analyses for Blackstone Insurance Solutions

- Assist with development, calibration, prototyping and documentation of risk models

- Assist with preparation of materials for Client and senior management

- Assist with development of robust risk management framework and analytics for BIS portfolios

- Analyze time series data to identify any trends or errors/exceptions

- Help foster a culture of risk awareness in partnership with other stakeholders

- Keep up to date with topical issues in risk management

- Model validation, scenario analysis using Monte Carlo Simulations, and factor modeling

- Portfolio optimization based on risk adjusted metrics

- Volatility forecasting, credit risk modeling, and liquidity risk assessments

Qualifications:

- 6+ years of experience in investment risk analysis, with a strong focus on fixed income

- Quantitative background such as Mathematics, Mathematical Finance, Econometrics, Data Science, Statistics

- Understanding of different alternative asset classes and instruments, with strong knowledge of fixed income securities and their characteristics in particular

- Experience of risk analytic platforms (e.g., FactSet; Risk Metrics; Bloomberg)

- Proficient in programming Python or R in the context of data science

- Advanced Modeling Techniques: Proficiency in using quantitative modeling techniques and software tools such as MATLAB, SAS, or specialized risk modeling software to build and calibrate complex risk models.

- Knowledge in various statistical testing and modeling techniques, including T-tests, ANOVAs, Regression Modeling, and Polynomial Approximations

- Machine Learning: Strong background in machine learning and predictive modeling, with the ability to apply machine learning algorithms to enhance risk assessment.

- Knowledge in various AI based techniques, including supervised/unsupervised models, reinforcement learning, GANs, CNNs/RNNs, and Generative AI

- Time Series Analysis: In-depth knowledge of time series analysis methods to identify trends, seasonality, and autocorrelation within financial data.

- Financial Engineering: Understanding of financial engineering principles, including derivatives pricing models, option pricing models, and fixed income analytics.

- Big Data Analytics: Familiarity with big data technologies and analytics platforms (e.g., Hadoop, Spark) to handle and analyze large volumes of financial data.

- Asset Liability Management (ALM): Familiarity with ALM principles and modeling techniques for matching assets and liabilities.

- VaR (Value at Risk): Proficiency in VaR modeling and understanding of its limitations in the context of fixed income.

- Asset Pricing Models: Knowledge of asset pricing models like the Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT).

- Risk Aggregation: Experience in aggregating risks across different asset classes to provide a comprehensive view of portfolio risk.

- Backtesting: Ability to perform rigorous backtesting of risk models to assess their predictive accuracy.

- Ability to work independently as well as thrive in a team-oriented environment

- Comfortable taking initiative and being resourceful

- Previous experience working in an investment risk or data science related role preferred

- Experience conducting data analysis of structured, semi-structured, and unstructured data using various data management tools such as Python, SQL, R

Key Requirements :

- Demonstrate a strong understanding of quantitative modeling concepts and their application to fixed income risk analysis.

- Ability to interpret and explain complex risk models and their implications to non-technical stakeholders, including senior management and clients.

- Proficiency in generating insightful risk reports and visualizations using tools like Tableau or Power BI to facilitate decision-making.

- Skills in data visualization techniques to present risk insights in a clear and understandable manner.

- Strong understanding of fixed income concepts

- Ability to apply quantitative methods to analyze risk

- Strong communication and presentation skills

- Ability to work independently and as part of a team

- Attention to detail and accuracy

Educational Qualification - Masters

Educational Qualification - Masters