Applied AI ML Data Scientist, Vice President – Payments

Overview

In this role you lead end-to-end ML/AI solutions for payments and banking operations, from discovery to production, shaping scalable, secure AI systems. You partner with product, risk, and technology teams to translate ideas into measurable outcomes and mentor others in production AI. You’ll drive generative AI and multi-agent approaches, deploy on cloud, and establish reusable data science capabilities that scale. This is a hands-on leadership role at the intersection of ML, operations, and governance, delivering data-led transformations at scale.

Responsibilities

• Lead end-to-end delivery of ML/AI solutions for payments and banking ops from discovery to production rollout
• Develop innovative ML solutions including generative AI and multi-agent approaches with evaluation, safety, and monitoring for production
• Own production deployment patterns: CI/CD, testing, model/prompts registries, governance, monitoring, rollback
• Architect and deploy scalable, secure ML/LLM services across APIs, batch, streaming, and event-driven patterns with SLAs
• Partner with product, operations, risk/control, and technology to align roadmaps and deliver data-led transformations
• Create reusable, modular DS/ML capabilities: feature engineering, evaluation harnesses, prompt tooling, agent frameworks, orchestration, memory management
• Provide technical leadership through code/design reviews and upskilling across DS/engineering partners
• Communicate model outputs, tradeoffs, and operational plans to stakeholders
• Maintain documentation for approaches, model cards, runbooks, and procedures

Key requirements

• Master’s degree in a quantitative field, or equivalent practical experience
• Strong applied ML fundamentals and data analysis skills
• Experience designing rigorous evaluation in real-world settings
• Experience deploying ML models in production at scale (monitoring, drift, performance, incident management)
• Strong Python software engineering skills (modular OO design, testing, debugging)
• Working knowledge of MLOps and LLMOps, distributed systems, training and serving patterns, feature stores, orchestration, scalable data processing
• Ability to design intrinsic/extrinsic evaluations aligned with business goals and guardrails for unintended outcomes
• Experience working in regulated environments with model risk, privacy, security, and audit-ready docs
• Strong stakeholder management and cross-functional collaboration

• Stakeholder management
• Team leadership and mentorship
• Clear communication with non-technical stakeholders
• NLP and generative AI, large language models, retrieval-augmented generation, tool and function calling, agentic workflows, multi-agent orchestration
• Orchestration frameworks, context and memory management, interoperability protocols (e.g., MCP)
• PyTorch, TensorFlow, scikit-learn, NumPy, pandas, SciPy, statsmodels

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