JPMorgan Chase

Vice President Data Scientist role in JPMorgan's Asset & Wealth Management Risk Analytics Group, focusing on developing and deploying advanced AI/ML and LLM solutions for risk management. Responsibilities include identifying use cases, leading model development (pre-training, fine-tuning, optimization), prompt engineering, quantization, evaluation, and collaborating on model serving systems. Requires strong Python, SQL, R, PyTorch/TensorFlow, AWS, and NLP/LLM experience, with frameworks like LangChain/LangGraph/AutoGen.

Quantitative trading and research role focused on RMBS and structured products, developing and maintaining models. The role involves applying machine learning and generative AI to credit modeling, data processing, model calibration, performance monitoring, and delivering analytics. Responsibilities include developing financial models, conducting back-testing, performing ML analysis on large datasets, building data analysis platforms, and collaborating with stakeholders.

This role focuses on applying AI/ML and quantitative techniques to options pricing, hedging, and quoting within financial markets. It involves developing and implementing sophisticated algorithms, quantitative models, and analytical tools for trading analysis and backtesting. The position requires experience with various machine learning techniques and programming languages like Python, KDB, C++, PyTorch, and Sklearn, with a strong emphasis on quantitative research and model development in a regulated financial environment.

Quant Modeling Associate role focused on ensuring fair lending compliance for various models, including traditional, ML/AI, and Gen AI models, within JPMorgan Chase's Risk Management and Compliance division. Responsibilities include statistical analysis, bias testing, developing mitigation algorithms, research, and documentation, with a focus on adhering to Fair Lending Laws and regulations.

The Quant Modeling Lead - Vice President role within JPMorgan Chase's Model Risk Governance and Review (MRGR) team focuses on the independent validation and risk governance of forecasting and scoring models, including those using advanced AI/ML techniques, for consumer banking applications. The role involves evaluating model conceptual soundness, performing independent testing, monitoring performance, and liaising with various internal and external stakeholders, including regulators.

Lead Software Engineer for Commercial and Investment Banking team, focusing on researching, developing, and productionizing high-performance machine learning and quantitative models, and designing scalable data processing pipelines. Requires deep ML/DL knowledge and experience with frameworks like TensorFlow/PyTorch, and ETL/real-time data processing.

Lead Data Scientist for Workforce Planning at JPMorgan Chase, focusing on developing and deploying AI/ML models for forecasting, capacity planning, and scheduling. The role involves full model lifecycle management, project leadership, stakeholder collaboration, and mentoring junior team members, with an emphasis on moving towards real-time inference and decision-making.

Senior Lead Software Engineer at JPMorgan Chase focused on building and enhancing technology products. The role involves providing technical guidance, developing secure production code, influencing product design, and acting as a subject matter expert. Requires experience in machine learning, NLP, training/fine-tuning LLMs, transformer architectures, and deploying AI systems, with proficiency in Python, TensorFlow, or PyTorch. Experience with scalable distributed systems, cloud-native environments, and security protocols is also necessary.

Lead Applied AI/ML role focused on developing and deploying models and analytical methods for compliance and risk management within a financial institution. Requires strong experience in Python, R, Scala, machine learning, statistical models, and specifically graph analytics and databases. The role involves data pipeline development, working with structured and unstructured data, and preparing technical documentation for governance review.

This role focuses on applying quantitative modeling and statistical analysis to ensure fairness and compliance in line-of-business models, including traditional, ML/AI, and Gen AI models, within a regulated financial environment. The primary goal is to detect and mitigate bias to comply with Fair Lending Laws and regulations.

Develops and industrializes quantitative methodologies and predictive models using statistical and machine learning techniques in R/Python for financial planning and forecasting within a large financial institution. The role involves data analysis, model development, documentation, and interaction with model governance.

Develops quantitative methodologies and predictive/explanatory models using statistical and machine learning techniques in R/Python for financial planning and forecasting within a large financial institution. The role involves data analysis, model development, documentation, and interaction with model governance.