JPMorgan Chase

Executive Director Data Scientist role focused on leading the strategy, delivery, and adoption of applied AI/ML solutions within JPMorgan Chase's Asset and Wealth Management team. The role involves hands-on ML engineering, MLOps, GenAI/LLMs, and team leadership to drive measurable business outcomes in financial services.

Lead Applied AI/ML role focused on delivering end-to-end ML capabilities for payments, including fraud detection, operational automation, and client experience. Responsibilities include designing, building, and deploying production-grade ML systems, defining evaluation metrics, establishing model lifecycle standards, and partnering with risk/compliance teams. Requires strong hands-on expertise in neural networks, Transformers, fine-tuning, distillation, and software engineering skills in Python with PyTorch/TensorFlow.

Lead the end-to-end delivery of advanced machine learning and AI solutions for Payments and Banking Operations, focusing on production deployment, scalability, and integration within a regulated fintech environment. This role involves developing innovative solutions including GenAI and agentic approaches, establishing MLOps practices, and providing technical leadership.

Lead a team of data scientists in building and deploying advanced AI solutions for image classification, text categorization, and intelligent data extraction from scanned documents within the healthcare provider team at JPMorgan Chase. The role involves managing the full ML lifecycle, from prototyping to production deployment on AWS EKS, with a focus on multimodal document understanding.

Executive Director role focused on leading the design and delivery of large-scale recommendation, ranking, and personalization systems for a consumer banking division. The role involves applying ML, including LLMs, to improve customer experiences, leading a team, and ensuring systems are reliable, secure, and scalable. Core focus is on recommenders and personalization, with pragmatic use of GenAI to augment the stack.

JPMorgan Chase is seeking an Applied AI/ML Senior Associate Machine Learning Engineer to join their Commercial & Investment Bank team. The role involves combining AI techniques with financial data to optimize business decisions and automate processes. Responsibilities include building scalable data science capabilities, deploying ML services, researching datasets, communicating AI capabilities, documenting processes for regulatory compliance, and collaborating on production architectures. The position requires a Masters or PhD in a quantitative discipline, strong ML theory, NLP specialization, MLOps knowledge, PyTorch experience, and cloud provider familiarity. Experience with containerization and model monitoring is also expected.

Lead Applied AI/ML Engineer at JPMorgan Chase in London, focused on building and deploying AI-powered products for the Commercial & Investment Bank. The role involves leveraging NLP and Computer Vision to optimize business decisions and automate processes, requiring a blend of research and software engineering skills, with a focus on productionizing ML models and services.

Executive Director role focused on architecting and leading the development of next-generation AI/ML solutions for credit card and banking business decisions, managing the full AI/ML lifecycle from design to production deployment, and leading a team of AI/ML scientists and engineers. Emphasizes deep learning, advanced modeling, MLOps, and cloud-native deployment within a regulated financial services environment.

Lead a team of AI/ML scientists and engineers to design, develop, and deploy AI/ML solutions for market operations in a large financial institution. Focus on end-to-end delivery, production deployment, MLOps, and stakeholder engagement.

Vice President role focused on building, deploying, and governing production AI/ML models, including LLMs and generative AI systems, within a large financial institution. The role emphasizes model governance, monitoring, and leveraging generative AI for productivity and efficiency, with a strong focus on regulatory compliance and cross-functional leadership.

Lead Generative AI Engineering role focused on designing, delivering, and improving production generative AI products and reusable backend APIs for firmwide adoption. This hands-on leadership position involves architecting scalable systems, setting technical direction for model-enabled services, ensuring reliability and responsible AI controls, and mentoring engineers. The role operates in production environments, emphasizing building at scale and driving execution against measurable outcomes.

This role focuses on designing, building, and supporting production generative AI products and reusable backend APIs within a large enterprise. It involves combining enterprise data with LLMs and multimodal models to deliver scalable, measurable solutions, collaborating with ML, cloud, and SRE teams, and ensuring reliability, performance, and operational controls. The role emphasizes hands-on engineering, real-world constraints, and high-impact delivery.

Lead Applied AI ML role in Fintech, focusing on developing and productionizing ML models and scalable data pipelines for personalized financial services. Requires strong CS background, end-to-end project ownership, and collaboration with cross-functional teams. Emphasis on research, algorithm implementation, experimentation, and ensuring regulatory compliance.

JPMorgan Chase's Chief Data & Analytics Office is seeking an Executive Director to lead Machine Learning initiatives. This role involves applying advanced ML techniques (NLP, speech, time series, RL, recommendations) to real-world problems, collaborating with cross-functional teams to deploy solutions, and developing large-scale frameworks for ML model application across the business. The position requires a strong background in ML/DL, experimental design, and communication skills, with a focus on production deployment within a regulated financial environment.

Director role focused on applying sophisticated machine learning methods (NLP, speech, time series, RL, recommendation systems) to real-world problems within a financial services context. The role involves research, model development, collaboration with business and technology partners, and deploying solutions into production. Emphasis on deep learning expertise, analytical thinking, and driving firm-wide ML initiatives.

Senior Associate role focused on applying AI/ML techniques, particularly NLP, to solve business problems within the Asset & Wealth Management division of JPMorgan Chase. Responsibilities include developing, productionizing, and documenting AI/ML solutions, collaborating with stakeholders, and coaching team members. Requires 3+ years of applied experience in data science and ML, with strong Python skills and experience with ML libraries and big data tools.

Lead Applied AI/ML role at JPMorgan Chase, focusing on developing and productionizing AI/ML solutions for business needs within the HR Data and Analytics team. Requires strong Python, ML techniques, and NLP experience.

Senior Associate role focused on applied AI and quantitative modeling within the Consumer Business Modeling team at JPMorgan Chase. The role involves contributing to the full modeling lifecycle, from defining objectives to developing and communicating results using advanced analytics on large-scale consumer data. Requires an advanced degree in a quantitative field and experience in developing machine learning, optimization, or statistical models.

Lead the development and deployment of AI/ML models for financial services, focusing on retail, credit card, home, auto lending, and wealth management. This role involves end-to-end project leadership, stakeholder management, and ensuring regulatory compliance.

Executive Director Data Scientist at JPMorgan Chase leading a team focused on AI, machine learning, and advanced analytics for the Card business. The role involves defining AI strategy, delivering AI/ML solutions from ideation to production, supporting customer experience, driving experimentation, and leading a team of analytics professionals. Requires strong leadership, technical depth, and business acumen, with a focus on driving measurable business impact and competitive advantage.

Software Engineer II on the Machine Learning Operations team at JPMorgan Chase, focusing on building and maintaining end-to-end ML solutions for customer-facing experiences. The role involves engineering scalable features and pipelines for relevance and ranking models, implementing ML Ops practices, and ensuring system reliability and observability in a production environment.

Software Engineer III with Python, AWS, and GenAI experience at JPMorgan Chase in Bengaluru. This role involves partnering on LLM projects, collaborating on ML products, and developing scalable ML-driven products within a regulated environment. Requires strong Python, cloud (AWS/Azure), and SDLC experience.

Data Scientist at JPMorgan Chase responsible for building, implementing, testing, and analyzing ML/AI products and solutions. The role involves leveraging statistical and mathematical techniques, including AI/ML and optimization, to solve business problems, designing controlled experiments, and translating business needs into data models. The position requires experience with SQL, pandas, data visualization libraries, and implementing traditional machine learning algorithms for predictive analytics.

Lead the development and deployment of machine learning solutions for a branch channel analytics team, focusing on a multi-action prioritization framework to optimize banker outreach. This role involves designing, developing, and deploying ML models, serving as a subject matter expert in ranking/recommendation systems and multi-objective optimization, and owning the full code development lifecycle.

Senior Associate role focused on applied AI and quantitative modeling within the Consumer Business Modeling team at JPMorgan Chase. The role involves leveraging large consumer datasets to solve complex business problems using the full modeling lifecycle, from defining objectives to communicating results. Requires advanced degree in a quantitative field and experience in developing ML, optimization, or statistical models.

Lead the development and deployment of AI/ML models for financial services, focusing on customer needs in areas like credit card, home lending, and wealth management. This role involves end-to-end project leadership, stakeholder management, and ensuring regulatory compliance.

Develops and deploys AI/ML models and generative AI solutions within web platforms and reporting tools for a financial services company, focusing on improving data delivery and user experience. The role involves mentoring other developers and integrating AI into existing analytics platforms.

Lead the design and delivery of AI/ML solutions for Market Operations at JPMorgan Chase. This role involves managing a team, partnering with business and technology stakeholders, and ensuring the successful implementation and scaling of AI-driven tools. The focus is on developing and deploying production-grade AI/ML applications, establishing best practices for evaluation and monitoring, and driving measurable impact.

Develop and launch AI/ML models for consumer banking, focusing on retail network optimization, investment, and workforce effectiveness using geospatial AI, graph-based models, and reinforcement learning. This VP-level role requires end-to-end project leadership, stakeholder management, and translating technical outputs for business partners, while ensuring regulatory compliance.

Quantitative Researcher (VP) at JPMorgan Chase focusing on Derivatives Modelling, Financial Engineering, and Data Science to enhance risk and pricing analytics for the Clearing business. The role involves leading the design, delivery, and governance of models, including Initial Margin methodology, and applying machine learning to transform risk management and automation. Responsibilities include end-to-end initiative leadership, model ownership, building and productionizing analytics, and leading ML/AI solution development.

This role is for an Applied AI/ML Engineer (VP level) at JPMorgan Chase, focusing on developing and delivering AI solutions for Commercial & Investment Banking Payments. The engineer will collaborate with cross-functional teams to boost operational efficiency and drive business innovation using AI/ML technologies, managing the code lifecycle from development to production.

Applied AIML Engineer at JPMorgan Chase, focused on developing and delivering AI-driven payments solutions for Commercial & Investment Banking. The role involves coding, cross-functional collaboration, and end-to-end code lifecycle management, with a minimum of 2 years of AIML project delivery experience.

This role focuses on validating and governing AI/ML models, including Generative AI and LLMs, within a financial services context. The associate will conduct independent model validation, assess adherence to development standards, identify risks, and communicate findings. A key responsibility is designing, building, and testing LLM-based use cases to improve internal processes, involving prompt engineering, RAG architectures, and agentic AI systems. The role requires a strong quantitative background, experience in model validation or development, proficiency in Python and ML libraries, and experience with Generative AI applications.

Associate role focused on developing, deploying, and monitoring machine learning applications and models within the Payments Operations team at JPMorgan Chase. The role involves identifying automation opportunities, processing multimodal data, building ML models, and ensuring their productionization and evaluation, with a strong emphasis on translating business needs into technical solutions and managing projects from inception to production.

Senior Associate role focused on applying and deploying ML solutions within JPMorgan's Commercial & Investment Bank, specifically in Payments Operations. The role involves researching, developing, and scaling ML capabilities, collaborating with engineering teams for deployment, and communicating results to stakeholders.