Data Scientist Lead - Vice President – Payments Data and Analytic

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 Summary
As 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
Required Qualifications, Capabilities, and Skills
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

Skills
Python, SQL, Machine Learning, Generative AI