Manager -Data Science

The AIM (Analytics, Investment & Marketing Enablement) team – a part of Global Commercial Service (GCS) Marketing– is the analytical engine that enables GCS business portfolio of American Express. Accelerating growth momentum, increasing profitability, and powering up our value proposition are key objectives for this organization. It is the analytical engine that powers profitable business growth through consultative and innovative solutions across GCS. The team enables GCS Marketing business by providing actionable insights to drive business strategy and growth.

This role will lead the development of advanced analytics and GenAI solutions that strengthen core technical capabilities across GCS Acquisition, Personalization, Engagement, and US SME distribution efficiency. The primary focus will be translating state-of-the-art (SOTA) GenAI technologies into scalable, production-grade solutions that drive measurable business impact. This includes real-time targeting, next-best-action decisioning, conversational optimization, automated insights, and GenAI-powered marketing productivity tools. The role requires ownership from research and experimentation through deployment and value realization, ensuring innovative AI capabilities directly improve Marketing performance and Sales effectiveness.

The ideal candidate combines deep technical expertise in ML/LLM systems with strong business acumen and execution capability. This person must be able to evaluate emerging GenAI advancements (e.g., LLM fine-tuning, RAG architectures, reinforcement learning, agent-based systems, recommendation systems, sequence mining models), rapidly prototype solutions, and translate them into robust, scalable applications within enterprise constraints. Strong hands-on proficiency in Python and SQL is essential, along with demonstrated experience deploying ML/GenAI solutions in production environments. Equally important is the ability to connect technical innovation to business outcomes—quantifying value, designing experiments, influencing stakeholders, and ensuring adoption across Marketing and Sales teams.

• Design and implement real-time targeting and personalization solutions that enhance acquisition and engagement performance.
• Develop next-best-action and treatment optimization frameworks leveraging ML, GenAI, and reinforcement learning approaches.
• Build GenAI-powered capabilities such as Marketing Assistants (e.g., Text-to-SQL, automated insights, conversational intelligence) to improve team productivity and distribution efficiency.
• Incorporate new behavioral, engagement, and AI-generated signals into prioritization and decision engines for US SME and Marketing channels.
• Lead experimentation, performance measurement, and optimization frameworks to quantify incremental impact and continuously refine models.
• Partner cross-functionally with Marketing, Sales, Product, and Technology teams to ensure successful implementation and measurable business outcomes.
• Continuously evaluate emerging SOTA GenAI techniques and translate them into practical enterprise solutions that create competitive advantage.

Minimum Qualifications

• Master’s or PhD in a STEM field (Computer Science, Engineering, Statistics, Mathematics, Physics, or related quantitative discipline).
• Strong industry experience building and deploying ML and/or LLM-based solutions in production environments.
• Advanced proficiency in Python and SQL, with hands-on experience developing, testing, and iterating on scalable data and ML systems.
• Demonstrated experience implementing GenAI use cases such as prompt engineering, retrieval-augmented generation (RAG), model evaluation, and integration with structured data systems.
• Strong analytical and problem-solving skills, with the ability to structure ambiguous business problems and deliver actionable insights.
• Proven ability to quantify business impact, design experiments, and communicate findings clearly to senior stakeholders.
• Experience working in cross-functional, fast-paced environments with strong ownership and accountability.

Preferred Qualifications

• PhD with strong research depth and proven track record of translating advanced AI research into commercial applications.
• Experience with reinforcement learning, LLM fine-tuning, lightweight RL approaches, or sequence-based decision optimization.
• Familiarity with modern LLM frameworks (e.g., LangChain, Huggingface, Nemo, Pytorch, etc.) and agent-based architectures, including debugging and monitoring multi-step workflows.
• Experience building recommendation systems, sequence mining models, personalization engines, next-best-action systems, conversational AI solutions, or marketing decision platforms.
• Strong understanding of experimentation frameworks, causal inference, and performance optimization in digital marketing or distribution contexts.
• Demonstrated ability to bridge cutting-edge AI research and enterprise deployment, ensuring scalable implementation and sustained business value generation.