JPMorgan Chase

Lead a team of data analysts to support data annotation and labeling for machine learning models in a fintech domain. This involves developing strategies for training data optimization, identifying patterns in conversational data using NLP, and evaluating the quality of ML outputs. Requires experience in ML data annotation, data modeling, and Python.

PhD-level Quant Researcher with 0-3 years of experience in machine learning, specializing in reinforcement learning, to develop alpha signals and portfolio construction methodologies for global equity markets. Responsibilities include data analysis, model enhancement, and integrating research into production systems.

Lead Data Engineer responsible for designing, building, and maintaining scalable data pipelines and infrastructure for processing scanned document images, integrating OCR and computer vision models, and managing data storage on AWS. The role involves collaborating with data scientists and ML engineers, ensuring data quality and governance, and leading a team of data engineers.

This role focuses on developing and managing intraday liquidity analytics using AI/ML tools to forecast cash positions and manage limits within JPMorgan Chase's Corporate Treasury. The primary responsibility involves utilizing Python and ML to publish daily forecasts, derive insights from payments data, and maintain a back-testing framework for forecast accuracy. Experience with ML or time-series forecasting in financial workflows is required.

Executive Director to shape the enterprise AI data strategy, focusing on knowledge representation, ontologies, and data foundations for AI applications at scale within a financial services organization.

Senior Director of Software Engineering for AI Infrastructure Platform at JPMorgan Chase, leading multiple departments to deliver a unified AI infrastructure layer across on-premises, public cloud, and accelerated-compute vendors. Owns training and experimentation on a Kubernetes-standardized platform, acting as a design partner for architectural trade-offs and ensuring reliable, secure, and operable systems at enterprise scale. Responsibilities include standardizing AI developer workflows, building platform APIs, improving GPU availability and utilization, defining scheduling and placement strategies, embedding security and controls, driving operational excellence, and leading engineering teams.

Quantitative researcher for an Equity Derivatives team, focusing on end-to-end alpha research and strategy deployment. Responsibilities include feature engineering, building calibration/attribution/monitoring frameworks, and implementing systematic strategies using data analytics, statistical modeling, and machine learning. The role also involves leveraging AI/ML tooling to accelerate research and improve productivity.

Quantitative Equity Researcher (VP) at JPMorgan Asset Management, focusing on developing novel alpha signals and enhancing return forecasting models using advanced statistical, econometric, and machine learning techniques on large datasets. The role involves research in portfolio construction and risk management, translating insights into investment strategies, and collaborating with technology teams for model integration.

Senior quantitative portfolio manager for an equity team, responsible for setting the quantitative research agenda, owning core portfolio analytics and risk frameworks, and driving implementation of systematic, factor-based, and data-driven insights for an $80bn equity portfolio. The role involves translating complex quantitative work into investment decisions, leading the integration of quantitative signals with fundamental views, owning risk model applications, designing portfolio construction frameworks, driving development of research and analytics tooling (Python-first), and evaluating/applying machine learning/AI techniques for feature engineering, ensemble methods, and NLP for alternative data, with an emphasis on interpretability and investment relevance.

Associate in Valuation Control Group focused on stress testing and predictive modeling for financial risk management. Develops models using Python, Excel VBA, and Alteryx, collaborating with various teams and interacting with regulators. Requires strong knowledge of statistical analysis, time series modeling, and applied machine learning.

This role focuses on building data and analytics products for Finance at JPMorgan Chase. The Data Science Product Senior Associate will partner with product, data science, and engineering teams to deliver user-centric capabilities that improve data quality and accelerate trusted reporting. The role involves shaping model-powered features, dashboards, and controls, and supporting the development and testing of AI/ML models and their safeguards. Key responsibilities include translating business problems into analytical requirements, analyzing product usage and model performance, building dashboards, partnering on data validation and control alignment, and managing a backlog of data enhancements. The role requires a Bachelor's degree in a quantitative field, experience with SQL, data visualization, Python/R for analysis and model evaluation, and familiarity with cloud platforms like AWS and Databricks. Experience with experimentation, A/B testing, and data/business intelligence concepts is also necessary. The role emphasizes improving data quality, accelerating reporting, and ensuring compliance with model risk standards.

This role focuses on optimizing fraud prevention strategies within a financial institution, leveraging AI/ML and graph analytics. The core responsibilities involve analyzing fraud drivers, developing strategic recommendations, monitoring and maintaining fraud authorization strategies, and leading roadmap initiatives. The role requires developing new model input variables and expanding data capabilities to improve authorization accuracy, while also ensuring compliance and collaborating with various internal teams.

The role involves quantitative research for portfolio analytics within Wealth Management. Responsibilities include developing and running risk and performance attribution models for multi-asset portfolios, building and maintaining models using Python and MATLAB, and applying statistical and machine learning techniques to investment research. The role also involves data management, model documentation, testing, and validation, as well as collaboration with portfolio managers and technology teams.

This role involves applying AI and machine learning techniques to enhance investment research and portfolio management within a Wealth Management Chief Investment Officer team. The responsibilities include risk modeling, portfolio construction, and quantitative research using Python and large datasets, with a focus on equity markets.

Software Engineer III focused on developing and enhancing critical software applications, with responsibilities including building ML execution pipelines using PySpark, Databricks, Python, and Streamlit, as well as real-time model execution processes. The role involves system design, development, troubleshooting, and contributing to software engineering communities.

Lead Software Engineer for JPMorgan Chase's Wholesale Credit Risk Technology Analytics Team, focusing on implementing critical technology solutions, developing secure production code, and identifying automation opportunities. Requires Python and Java proficiency, agile methodology knowledge, and cloud-native experience. LLM Engineering experience is preferred.

This role is accountable for data in AI/ML products, focusing on data quality, governance, and ensuring data is AI/ML ready. The Data Owner collaborates with various teams to understand data needs, identify sources, manage dependencies, and publish data to central platforms, ultimately enabling faster innovation through data.

Develop and enhance quantitative models for wholesale credit risk measurement, credit loss estimation, and stress testing. Implement numerical methods and high-performance computing solutions, analyze large datasets, and apply machine learning techniques within a financial services context. The role requires collaboration with business, finance, and technology teams, and communication with senior stakeholders and regulators.

This role focuses on developing, refining, and maintaining quantitative models, including time series forecasting, econometric, and machine learning models, for Home Lending Finance at JPMorgan Chase. The Senior Associate will ensure alignment with firm's model risk guidelines and regulatory requirements, collaborate with stakeholders, and potentially introduce new technologies. The role requires a Master's degree in a quantitative field and 5+ years of experience, with proficiency in Python and SQL.

Develops and maintains complexity modeling and end-to-end forecast modeling for marketing campaigns, leveraging statistical and machine learning methods for analysis, forecasting, and business strategy optimization. Focuses on predictive modeling, data modeling solutions, and forecasting techniques.

This role supports a Commodities Systematic Trading team, focusing on alpha generation, e-trading, and data science/machine learning within financial markets. Responsibilities include building and maintaining tools, UIs, and frameworks for systematic risk-taking, developing systematic strategies and performance monitoring systems, enhancing tool robustness, processing and analyzing large datasets for alpha research, and collaborating with traders. Requires strong Python and data analysis skills, database knowledge (KDB), and excellent communication. Preferred qualifications include experience in systematic/quant research and data cleaning/processing.

Lead role focused on making data available and AI-ready for financial services (Asset & Wealth Management). Responsibilities include data provisioning, lineage, metadata enrichment, data quality, and embedding controls to support AI/analytics initiatives and meet regulatory requirements. Requires strong data management, data science, and leadership skills within a complex financial environment.

The role focuses on developing and implementing data analytics and data science solutions within the Internal Audit department of JPMorgan Chase. Responsibilities include sourcing, cleansing, and staging data, building analytical tools, and supporting data science initiatives to enhance audit testing and efficiency. The role requires experience with large datasets, SQL, Python, and data visualization tools, with a focus on delivering sustainable analytical solutions.

This role focuses on developing and maintaining credit risk grading models and frameworks for Wholesale Credit Risk clients. It involves quantitative work, methodology design, data pipeline development using Python, and applying LLMs to synthesize unstructured data for model development. The role also requires translating analytical findings for senior leadership and regulators, and partnering with technology and control teams.

This role focuses on designing, engineering, and optimizing Databricks DDL models for analytics, ML, NLP, and LLM use cases within a financial domain. It emphasizes performance engineering, data quality, reconciliation, and documentation, with a secondary focus on enabling advanced analytics and NLQ.

This role focuses on transforming and optimizing credit forecasting processes within JPMorgan Chase's Consumer & Community Banking division. It involves integrating advanced analytics, implementing new tools and methodologies, and leveraging prompt engineering with GenAI capabilities. The associate will work on streamlining processes, automating tasks in Databricks, creating data pipelines, and managing transformation projects. Key responsibilities include defining requirements for semantic layers, enabling AWS services for analytics, and ensuring adherence to firm-wide controls and governance. The role also involves facilitating training on cloud capabilities and analytical tools.

JPMorgan Chase (55ip) is seeking a Senior Associate Python Engineer to join their Quantitative R&D team. The role involves developing and growing a quant platform for historical and forward-looking simulations, backtesting, and machine learning. The engineer will work with quant teams to design and implement scalable cloud-native solutions, focusing on full-stack development with Python, AWS, and related technologies. Responsibilities include building high-performance web applications, producing architectural diagrams, participating in agile development, and ensuring code quality.

Senior Lead Data Engineer at JPMorganChase within the Commercial and Investment Banking, focusing on building and operating AWS/public cloud-based applications. The role involves developing secure production code, driving product design decisions, and acting as a subject matter expert in cloud and AI/ML software practices. Requires extensive experience in AWS, strong Python skills, and familiarity with MLOps and CI/CD. Emphasis on driving adoption of AI engineering tools for productivity gains.

Senior Lead Software Engineer focused on Data/Machine Learning Operations within a financial services firm. The role involves designing, developing, and deploying large-scale cloud solutions, migrating legacy applications, and creating tools for automation and monitoring. Requires strong software engineering skills, cloud expertise, and good knowledge of ML modeling as an engineer.

This role leverages data governance, predictive analytics, data science, and machine learning to support strategic initiatives in Collections and Recovery Operations within a financial services division. The associate will analyze data, monitor trends, and implement strategies to enhance performance and manage expenses, utilizing programming languages and visualization tools.

Lead Data Scientist role focused on payments transformation, driving business insights through data analysis, automated reporting, and product analytics. The role involves quantitative analysis, data wrangling, visualization, and supporting ML algorithm prototyping within a fintech domain. It requires strong SQL, data visualization tools, and consulting skills, with a preference for ML techniques and financial services experience.

This role supports the development and enhancement of credit risk grading models and frameworks for Wholesale Credit Risk. It involves quantitative work, model methodology, data preparation, building Python pipelines for analytics, and applying LLMs to synthesize unstructured data. The goal is to enhance credit risk assessments and identify concentration risks.

This role focuses on building and scaling data products and pipelines for analytics and AI/ML within Investment Banking. The Vice President will own a portfolio of data products, build and operate data pipelines, and enable AI/ML workloads by provisioning features and labeled datasets. The role emphasizes data governance, quality, and compliance, with a strong focus on product thinking and delivering actionable insights through various consumption channels.

This role is for a Data Science Product Associate at JPMorgan Chase, focusing on building data and analytics products for Finance. The role involves translating business needs into analytics-driven features, collaborating with product, data science, and engineering teams, and supporting the development and testing of AI/ML models and their controls. Key responsibilities include analyzing product usage, building dashboards, ensuring compliance with data and model risk standards, and managing backlogs using Agile practices. The role requires a quantitative background, proficiency in SQL, Python/R for analysis and model evaluation, experience with experimentation, and knowledge of data and BI concepts.

This role focuses on developing and implementing machine learning models for wholesale credit risk management at JPMorgan Chase. Responsibilities include leading model development, designing statistical and econometric models, developing stress capital models (CCAR, ICAAP, CECL), and utilizing AI/ML techniques for risk monitoring and predictive modeling on large datasets. The role requires strong programming skills in Python and knowledge of credit risk modeling, with experience in regulatory reviews being essential.

Lead Software Engineer focused on Python, Big Data, AWS, and ETL pipelines within a fintech environment. The role involves designing, developing, and optimizing data pipelines, analytics workflows, and data models, with a strong emphasis on leveraging AI, machine learning, and advanced analytics for risk insights and anomaly detection. The engineer will also champion cutting-edge technologies and ensure data quality, security, and governance.

This Vice President role focuses on data ownership and governance within a financial institution, aiming to enable AI/ML use cases by ensuring data quality, defining critical data, and developing AI-ready data products. The role involves managing data lifecycles, mitigating risks, and collaborating with technology and business partners to deliver impactful data solutions.

JPMorgan Chase is seeking a Quantitative Investment Analyst to join their Equity Portfolio Management team. The role involves combining fundamental equity analysis with quantitative and factor-based approaches, supporting risk modeling, portfolio construction, and quantitative research using Python/R. The candidate will also contribute to the application of AI/ML techniques to enhance investment research and portfolio management.