Data Scientist

Applied AI/ML - Vice President in Wholesale Payments OperationsJoin a team applying modern artificial intelligence and machine learning to high-impact, high-scale payments workflows. You will work with large datasets and complex operational processes to deliver measurable outcomes. You will build production-grade solutions spanning natural language processing, document understanding, and LLM-enabled applications. You will collaborate closely with business and technology partners to take ideas from concept to deployment. You will help raise engineering standards and mentor others while shipping real solutions.Wholesale Payments supports global client payments across multiple methods, currencies, and geographies. The role focuses on building scalable AI/ML capabilities for operations use cases, including document processing and workflow automation. You contribute to reusable platforms and patterns that enable teams to safely deploy and operate models in production.Job responsibilitiesPartner with senior business stakeholders to frame problems, define success metrics, and align AI/ML roadmaps to business prioritiesLead architecture, design, and end-to-end delivery of enterprise AI/ML solutions for Wholesale Payments OperationsWrite clean, performant, production-quality code and set engineering standards across the teamChampion modern software development life cycle, continuous integration and continuous delivery, and DevOps practicesDeploy and operate AI/ML services on AWS at scaleApply advanced techniques including data and text mining, document analysis, classification, optical character recognition (OCR), natural language processing (NLP), and LLM workflows (including retrieval-augmented generation and fine-tuning)Design and implement scalable, secure data pipelines to support model training and inferenceDefine and enforce MLOps, model governance, monitoring, and responsible AI practices; represent the team in architecture and risk forumsEvaluate model performance in production, including drift management and reproducibilityMentor engineers, conduct code and design reviews, and support recruiting and talent developmentRequired qualifications, capabilities, and skillsMaster's degree in Mathematics, Computer Science, Engineering, or a related quantitative field6 years of professional AI/ML experience delivering production systems4 years of advanced Python development in production environments, including use of AI-assisted coding tools to improve productivity while preserving code quality4 years of hands-on experience designing and deploying production machine learning systems on Amazon Web Services (AWS) (for example: SageMaker, Lambda, ECS/EKS, S3)Demonstrated experience delivering AI/ML solutions with measurable business outcomes at scaleExperience with object-oriented design, distributed systems, and performance engineeringDemonstrated experience building and deploying LLM-based applications, including retrieval-augmented generation and fine-tuning workflowsHands-on experience in natural language processing (NLP), computer vision, optical character recognition (OCR), or document AI solutions in productionExperience implementing MLOps practices using tools such as MLflow, Kubeflow, Airflow, feature stores, or model registriesDemonstrated experience mentoring engineers and driving execution against multi-quarter roadmapsStrong communication skills, including translating business needs into technical deliverables for senior stakeholdersPreferred qualifications, capabilities, and skillsExperience delivering AI/ML solutions in wholesale payments, transaction banking, or financial servicesExperience with model risk management frameworks, model governance, and responsible AI practicesExperience with Kubernetes and infrastructure-as-code (for example: Terraform)Experience with real-time or streaming inference use casesContributions to open-source machine learning ecosystems or peer-reviewed publications