JPMorgan Quantitative Research Associate - Asset & Wealth Management 

India, Karnataka, Bengaluru 

63391188

Job responsibilities :

• Contribute to the research and enhancement of the risk methodology for Asset and wealth management Risk Analytics. The methodology covers sensitivity, stress, VaR, factor modeling, and Lending Value pricing for investment (market), counterparty (credit), and liquidity risk.

• Work with peers and stakeholders to identify use cases and opportunities for Data Science to create value. Use your knowledge of Computer Science, Statistics, Mathematics and Data Science techniques to provide further insights into security and portfolio risk analytics.

• Assist with continuous improvements in our adopted Machine Learning and statistical technics used in our data and analytics validation process.

• Collaborate, design, and deliver solutions that are flexible and scalable using the firm’s approved tools.

• Prepare comprehensive model documentation for the Model Risk Governance and Review group to validate the models our team owns and uses, along with ongoing monitoring and back testing.

• Contribute to the analysis of new and large data sets and assist with their onboarding, following our best practice data model and architecture using big data platforms.

Required qualifications, capabilities, and skills :

• Minimum 2 yrs Data Scientist or equivalent role.

• A quantitative, technically proficient individual who is detail-oriented, able to multi-task, and work independently.

• Excellent communication skills.

Preferred qualifications, capabilities, and skills :

• A strong understanding of statistics, applied AI/ML techniques, and a practical problem-solving mindset. Practical experience in financial markets in a quantitative analysis/research role within Risk Management, a Front Office role, or equivalent is a plus.
• Knowledge of asset pricing, VaR backtesting techniques, and model performance testing is a plus.
• Knowledge in modular programming in SQL, Python, ML, AWS Sagemaker and TensorFlow is preferred.
• A degree in a quantitative or technology field (Economics, Maths/Statistics, Engineering, Computer Science or equivalent) is preferred.