JPMorgan Risk Management - Asset Wealth -Risk Analytics – Data Scientist Associate 

United States, New York 

23789833

Job responsibilities

• 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.

• Lead continuous improvements in our adopted artificial intelligence/machine learning (AI/ML) 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 new technologies and tools, such as AI and large language models (LLMs). Use citizen developer journey platform to find efficiencies in our processes.

• 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.

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

Required qualifications, capabilities, and skills

• 2+ years experience as a Data Scientist or in an adjacent quantitative role.

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

• Effective communication skills to clearly explain complex concepts.

• A strong understanding of statistical models, applied AI/ML techniques, and a practical problem-solving mindset.

• Knowledge in modular programming in SQL, Python, ML, AWS Sagemaker, TensorFlow, Bitbucket, GitHub or alike.

Preferred qualifications, capabilities, and skills

• 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.

• A degree in a quantitative or technology field (Economics, Maths/Statistics, Engineering, Computer Science or equivalent)