Principal Data Scientist-Payments-Executive Director

JPMorgan Chase & Co.•Wilmington, DE

1d•$171,000 - $260,000

About The Position

Treasury teams are under pressure to make faster decisions with better data—without increasing risk. In this role you will drive the data science and AI efforts that help shape the future of the corporate treasury. As a Principal, Data Scientist in the Payments Data & Analytics organization you will partner with the product organization in developing and scaling agentic, AI-native treasury products, grounded in real practitioner workflows.

Requirements

PhD in a quantitative discipline (e.g., computer science, data science, statistics, econometrics, or related) with 10+ years of progressive analytics and data science experience spanning both hands-on development and enterprise-scale leadership responsibilities
Deep technical expertise across applied data science, including predictive modeling, statistical analysis, customer/behavioral segmentation, and experimental design, with the ability to coach others and establish engineering-quality standards for analytic work
Languages & Modeling skills: Python, JavaScript, PHP, SQL, C#, Predictive & Causal Modeling
AI/ML Platform expertise: MLOps (MLFlow, Metaflow, DataRobot), Generative AI, AI Observability, NLP
Proven track record translating machine learning and analytics into measurable business outcomes, including defining the decision to be improved, building the model/measurement approach, and driving adoption through product, operations, and executive stakeholders
Strong strategic and commercial acumen, with the ability to frame ambiguous analytical questions into executable roadmaps and translate findings into executive-ready narratives, trade-offs, and recommendations
Experience leading analytics across multiple concurrent business domains (e.g., product, customer lifecycle, operations, growth, or planning), balancing near-term delivery with longer-term capability building (data foundations, tooling, and reusable methods)
Exceptional stakeholder management and communication skills, including influencing senior leaders, aligning cross-functional partners, and managing competing priorities while maintaining trust and momentum

Nice To Haves

Experience applying analytics to global scale, B2B digital products and lifecycle management, including acquisition, onboarding, engagement/retention, segmentation-driven personalization, and monetization decisioning
Exposure to modern generative AI approaches (e.g., large language models, retrieval-augmented generation patterns, and workflow/agent concepts), with the ability to evaluate feasibility, risk, and business value even when not acting as the primary model developer
Practical experience with causal inference and experimentation at scale, including A/B testing, uplift measurement, and designing experiments that work within real-world product and operational constraints
Familiarity with responsible AI principles and analytics governance (e.g., model risk management concepts, documentation, monitoring, and bias/robustness considerations), with the judgment to operate effectively in more regulated or higher-reputational-risk environments
Demonstrated ability to drive analytics adoption through organizational enablement, such as self-service measurement tools, standardized metric definitions, data stewardship practices, and change management for new decision processes
Comfort operating in Agile/product-oriented delivery models, partnering with product and engineering teams to translate business problems into iterative roadmaps and measurable releases

Responsibilities

Define and drive the AI strategy for client facing agentic corporate treasury solutions, identifying high-value opportunities for generative AI, agentic AI, and analytics innovation to create competitive advantage
Partner with Data, Product, and Technology to deliver AI and machine learning solutions from ideation and prototyping through production deployment, ensuring solutions are scalable, responsible, and aligned to business needs
Stay current on emerging AI and machine learning techniques and translate new capabilities into practical applications for the Payments business
Drive evaluation frameworks, experimentation (including A/B testing and causal inference), and strategies that improve client experience
Attract and retain top analytics talent through hiring, onboarding, and skills development programs.