Principal Data Scientist-Payments-Executive Director

Ignite your passion for product innovation by leading customer-centric development, inspiring solutions, and shaping the future with your strategic vision and influence.

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.

Job 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. 

Required qualifications, capabilities and skills:

• 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

Preferred qualifications, capabilities and skills: 

• 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