Data Scientist Lead - Vice President - Payments Data and Analytic

JPMorganChase is building AI capabilities for treasury and payments within its Payments Data and Analytics team, partnering across the organization to turn advanced analytics into scalable, governed solutions. This role leads the design and delivery of AI, machine learning, and agent-based systems across the end-to-end data science lifecycle, from problem definition through monitoring and continuous improvement.

What you will do

• 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
• Create 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 with operations, risk, controls, legal, compliance, and sales to deliver solutions that are scalable, controlled, and client-focused
• 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

Required qualifications

• Bachelor's degree in a quantitative field (for example: Computer Science, Data Science, Statistics, Mathematics, Engineering, Economics, or Finance)
• 5+ 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
• 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

Technologies

• Python, SQL
• Large language models, Generative AI, AI agents
• Prompt engineering, Retrieval-augmented generation, Multi-agent workflows
• Databricks, AWS, Spark, Hive, Hadoop

Preferred qualifications, capabilities, and 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 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

Location: Jersey City, NJ (onsite)

Compensation: USD 147,250 - 215,000 per year

J.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments, serving corporations, governments, and institutions across more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk, and extends liquidity in markets worldwide.