Principal Data Scientist--Executive Director

Position: Principal Data Scientist-Payments-Executive Director
:

Category:
Predictive Science

Job Schedule:

Full time

Posted Date: T17:55:22+00:00

Job Shift:

Base Pay/Salary:
Wilmington,DE: $-$

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, Data Robot), 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