Join JPMorgan’s Asset & Wealth Management Risk Analytics Group and help shape the future of risk management through data science and AI. As a Vice President Data Scientist, you’ll 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 - Vice President in the AWM Risk Analytics Group, you will partner with senior team members to identify, design, and implement data-driven risk analytics solutions. You’ll 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 4 years’ 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.
- Bachelor’s 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 (Master’s 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.