AWM Quant Modelling- Senior Associate

Join JPMorgans Asset & Wealth Management Risk Analytics Group and help shape the future of risk management through data science and AI. As a Senior Associate Data Scientist youll collaborate with top quantitative and market risk professionals to deliver transformative analytics solutions. Your expertise will directly impact our risk methodologies and the Newton platform enhancing decision-making and operational efficiency. Be part of a team that values innovation technical excellence and continuous learning. Make a difference in a dynamic environment where your work drives real business outcomes.

As a Data Scientist - Senior Associate in the AWM Risk Analytics Group you will partner with senior team members to identify design and implement data-driven risk analytics solutions. Youll leverage your quantitative and technical skills to develop fine-tune and deploy advanced machine learning models supporting the evolution of our risk management systems and methodologies. This is a unique opportunity to work at the intersection of financial markets AI and big data driving innovation and efficiency across our Asset & Wealth Management business.

Job Responsibilities

• Identify and evaluate use cases for data science to enhance risk analytics and create business value.
• Lead the development and continuous improvement of AI/ML and statistical techniques for data validation and analytics.
• Design pre-train and fine-tune production-grade language models for hierarchical classification summarization and QA.
• Collaborate with stakeholders to deliver scalable flexible solutions using approved AI and LLM technologies.
• Perform prompt engineering quantization and evaluation to optimize large language model robustness and performance.
• Analyze and onboard new large data sets ensuring alignment with best practice data models and architecture.
• Partner with Technology teams to optimize model performance and deployment using customized training frameworks.
• Design and implement sophisticated model serving systems leveraging distributed systems and AWS cloud.
• Contribute to the research and enhancement of risk methodologies including sensitivity stress VaR factor modeling and Lending Value pricing.
• Support the full product development lifecycle from defining objectives to delivering key data-driven solutions.
• Communicate complex technical concepts clearly to both technical and non-technical stakeholders.

Required Qualifications Capabilities and Skills

• Minimum 2 years work experience as a Data Scientist or in an applied AI/quantitative role developing and deploying NLP and predictive models.
• Strong foundation in statistics applied AI/ML techniques and advanced problem-solving.
• Hands-on experience with distributed computing NLP (entity recognition text classification summarization QA) and LLM optimization.
• Proficiency in Python SQL R PyTorch or TensorFlow and AWS.
• Experience with frameworks such as LangChain LangGraph or AutoGen.
• Demonstrated ability to improve model robustness and conduct advanced statistical modeling and A/B testing.
• Detail-oriented able to multi-task and work independently in a fast-paced environment.
• Excellent communication and collaboration skills.
• Experience in modular programming and big data platforms.
• Bachelors degree in a quantitative or technology field (AI Mathematics Statistics Engineering Computer Science or equivalent).
• Proven track record of delivering data-driven solutions in a business context.

Preferred Qualifications Capabilities and Skills

• Experience in financial markets in a quantitative analysis research or risk management role.
• Knowledge of asset pricing VaR backtesting and model performance testing.
• Advanced degree (Masters or PhD) in a quantitative or technology discipline.
• Experience with model serving systems and distributed architectures.
• Familiarity with citizen developer platforms and process automation.
• Exposure to Front Office or equivalent financial roles.
• Demonstrated ability to drive innovation and efficiency in risk analytics.