Join the Payments Data and Analytics team to build the next generation of agentic treasury capabilities. In this role, you will shape how AI-enabled products improve cash management, liquidity insights, exception handling, and treasury decision support. You will work with product, engineering, operations, and control partners to deliver solutions that are scalable, measurable, and well-governed. You will help turn complex problems into intuitive experiences for clients and internal users. You will grow your leadership while driving meaningful business outcomes.
Job SummaryAs a Data Scientist, Vice President in Payments Data and Analytics, you will lead the design and delivery of AI, machine learning, and agent-based solutions that modernize treasury and payments capabilities. You will translate treasury and payments needs into data-driven products by partnering with product, engineering, operations, risk, and controls stakeholders. You will own delivery end-to-end—from problem framing and modeling through deployment, monitoring, and continuous improvement. You will set clear success metrics and ensure solutions meet expectations for quality, explainability, and control alignment. You will influence roadmap decisions through strong technical judgment and stakeholder leadership.
You will help build reusable patterns for agent orchestration, evaluation, monitoring, and human review that can scale across treasury use cases. You will be expected to make independent decisions, proactively identify risks and gaps in data or model behavior, and drive resolution with partners. Success in this role requires strong technical depth, a product mindset, and the ability to communicate clearly with both technical and non-technical audiences.
Job Responsibilities- Design, build, and deploy AI agents and agent-based workflows for treasury use cases, including cash positioning, liquidity forecasting, exception handling, and treasury insights
- Develop machine learning models for cash-flow forecasting, payment behavior analytics, working-capital insights, anomaly detection, and decision support
- Build reusable frameworks for orchestration, prompt design, retrieval-augmented generation, evaluation, monitoring, human review, and model governance
- Own the end-to-end data science lifecycle, including problem definition, data discovery, feature engineering, modeling, validation, deployment, monitoring, and continuous improvement
- Partner with product and engineering teams to translate AI opportunities into product features, technical requirements, and measurable business outcomes
- Collaborate across operations, risk, controls, legal, compliance, and sales to deliver scalable, controlled, client-focused solutions
- Monitor model and agent performance across accuracy, drift, latency, explainability, adoption, escalation rates, and business impact
- Review and validate AI-assisted outputs for accuracy, completeness, relevance, source consistency, and alignment with controls
- Identify and escalate concerns related to AI risk, data quality, privacy, controls, model risk, or automation when outputs are unsupported or inconsistent with source information
- Mentor junior data scientists and analysts on experimentation, evaluation, responsible AI, and stakeholder communication
- Bachelor’s degree in a quantitative field (for example: Computer Science, Data Science, Statistics, Mathematics, Engineering, Economics, or Finance)
- Five years or more of experience in data science, machine learning, applied AI, or advanced analytics
- Hands-on experience developing machine learning models and analytical pipelines for business-facing or client-facing use cases
- Hands-on experience with large language models, generative AI applications, AI agents, prompt engineering, retrieval-augmented generation, or multi-agent workflows
- Strong Python and SQL skills for data processing, analysis, modeling, experimentation, and automation
- Experience building data pipelines for ingestion, transformation, orchestration, feature engineering, validation, reconciliation, and monitoring
- Strong understanding of model evaluation, experimentation, statistical analysis, data quality practices, and performance monitoring (including drift detection)
- Ability to define model objectives, evaluation criteria, success metrics, and acceptance thresholds aligned to business outcomes
- Ability to translate ambiguous business problems into structured approaches, delivery plans, and measurable outcomes
- Strong understanding of responsible AI practices, including output validation, human oversight, documentation, and escalation of AI-related risks
- Strong communication and stakeholder-management skills, including the ability to explain complex concepts to technical and non-technical audiences
- Advanced degree in a quantitative discipline
- Experience in treasury, cash management, liquidity, payments, accounts payable, accounts receivable, or working capital
- Experience building or deploying agent-based solutions that integrate structured data, unstructured data, tools, APIs, and human review
- Experience with Databricks, AWS, Spark, Hive, Hadoop, or other distributed data and compute platforms
- Knowledge of data governance, model governance, responsible AI, information handling, auditability, and control expectations for AI-enabled capabilities
- Experience with forecasting, anomaly detection, natural language processing, optimization, graph methods, or treasury decision support analytics
- Financial services experience, ideally in payments, treasury, liquidity, receivables, payables, or transaction banking
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
Skills Required
- Bachelor’s degree in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics, Engineering, Economics, or Finance
- Five or more years of experience in data science, machine learning, applied AI, or advanced analytics
- Hands-on experience developing machine learning models and analytical pipelines for business-facing or client-facing use cases
- Experience with large language models, generative AI applications, AI agents, prompt engineering, retrieval-augmented generation, or multi-agent workflows
- Strong Python and SQL skills
- Experience building data pipelines for ingestion, transformation, orchestration, feature engineering, validation, reconciliation, and monitoring
- Understanding of model evaluation, experimentation, statistical analysis, data quality practices, performance monitoring, and drift detection
- Ability to define model objectives, evaluation criteria, success metrics, and acceptance thresholds aligned to business outcomes
- Ability to translate ambiguous business problems into structured approaches, delivery plans, and measurable outcomes
- Understanding of responsible AI practices, including output validation, human oversight, documentation, and escalation of AI-related risks
- Strong communication and stakeholder-management skills
- Advanced degree in a quantitative discipline
- Experience in treasury, cash management, liquidity, payments, accounts payable, accounts receivable, or working capital
- Experience building or deploying agent-based solutions integrating structured data, unstructured data, tools, APIs, and human review
- Experience with Databricks, AWS, Spark, Hive, Hadoop, or other distributed data and compute platforms
- Knowledge of data governance, model governance, responsible AI, information handling, auditability, and AI control expectations
- Experience with forecasting, anomaly detection, natural language processing, optimization, graph methods, or treasury decision-support analytics
- Financial services experience, ideally in payments, treasury, liquidity, receivables, payables, or transaction banking
JPMorganChase Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about JPMorganChase and has not been reviewed or approved by JPMorganChase.
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Healthcare Strength — Health coverage is considered comprehensive, including medical, dental, and vision, alongside wellness and mental health resources. Some locations add onsite health centers and related wellbeing support.
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Retirement Support — Retirement offerings include a 401(k)-type savings plan and related financial benefits, with options such as employee stock purchase participation. Financial planning resources are also highlighted to support long-term savings.
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Parental & Family Support — Paid parental leave of 16 weeks for birth or adoption is available for all parents. Child care and back-up child care resources further reinforce family support.
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JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $3.7 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world’s most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Technology fuels every aspect of our company and is at the heart of everything we do. With over 50,000 technologists globally and an annual tech spend of $12 billion, we are dedicated to improving the design, analytics, development, coding, testing and application programming that goes into creating high quality software and new products. Learn more about technology at our firm, explore resources from our Distinguished Engineers, AI & ML researchers, and other experts; access the latest episode of our TechTrends podcast, and more at www.jpmorgan.com/technology. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com. ©2023 JPMorgan Chase & Co. All rights reserved. JPMorgan Chase is an Equal Opportunity Employer, including Disability/Veterans.
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