Senior Manager-AI Innovation & Research

AI Labs seeks a Senior AI Researcher (Senior Manager) to lead a team of Ai researchers that will powers credit, fraud and marketing decisions. The candidate serves as a thought leader and a problem-solver who can blend business, technical, and industry best practices when it comes to developing the models, and algorithms that power our customers’ digital experiences. This role sits at the intersection of applied AI research, large-scale AI platforms, and large-scale compute infrastructure. The Sr. Manager will help define and execute a forward-looking research agenda and help to translate cutting-edge machine learning, and systems innovation into scalable, governed, and business-critical AI capabilities. Role is focused on creation and integration of foundational models that can provide value for multiple modelling usecases.

The ideal candidate will combine deep hand-on ML research expertise with strong practical knowledge and experience in building scalable infrastructure and data science platforms.

Colleagues in AI Labs serve as key members of the Global Decision Science organization within Credit and Fraud Risk at American Express. This critical team is responsible for managing enterprise risks throughout the customer lifecycle, across our consumer and commercial businesses, and across all our global products. We develop industry-first data capabilities, build profitable decision-making frameworks, create machine learning-powered predictive models, and improve customer servicing strategies.

Our Global Decision Science teams use industry leading modelling and AI practices to predict customer behaviour. We develop, deploy and validate predictive models and support the use of models in economic logic to enable profitable decisions across credit, fraud, marketing and servicing optimization engines.

• Lead and drive implementation of advanced AI research and integration into business in areas such as foundation models, graph-based methods, reinforcement learning, and causal-modelling techniques.
• Lead research and implementation of Tabular Foundational Models (TFMs) for structured data and modelling usecases.
• Lead research, development and implementation of Sequence-based foundational models for event streams, such customer actions and transaction histories.
• Partner with business users, technology and infrastructure teams to ensure solutions are flexible, secure, cost-effective, and compliant with enterprise standards
• Translate research outcomes into production-ready platforms, tools, and capabilities that are adopted at scale across the enterprise
• Provide thought leadership for research in Ai research and machine learning. Present and communicate research findings to senior leadership, influencing enterprise-level decision-making and strategic direction.
• Work in close partnership with other research teams in AI labs.
• Lead a team of Ai researchers and create a highly effective and engaged team culture focused on innovation.

• Advanced degree in Computer Science, Statistics, Operations Research, Engineering, Mathematics, Economics, Physics, or a related quantitative field or equivalent work experience. 4+ years of work experience in applied research lab organisations is required.

• Very strong record of applied research impact through platforms, patents, production systems, or enterprise-scale adoption, in addition to traditional publications.

• Expertise with GPU-accelerated ML workloads, high-performance computing, and advanced optimization techniques are essential.

• Expert knowledge of machine learning and statistical modeling methods for supervised and unsupervised learning in areas such as transformer and attention-based architectures, deep learning for structured sequential and graph data, multi-task and transfer learning.

• Exceptional programming skills in Python, Spark. Additional knowledge of C/C++ and Java is preferred

• Expertise in hybrid-cloud AI/ML infrastructure, including Google Cloud (e.g., Vertex AI, BigQuery, Dataproc) and large-scale distributed computing environments.

• Strong knowledge of databases and related languages/tools such as SQL, NoSQL, Hive, etc.

• Exceptional people leadership and managerial skills

• Ability to work in a dynamic, cross-functional environment, with a strong attention to detail

• Effective communication skills and ability to explain complex data products in simple terms

• Strong relationship building and collaborative skills.