Senior Manager - Data Science

American Express•New York, NY

About The Position

The Senior Manager will shape and execute data-driven strategies to enhance Sales effectiveness, strengthen portfolio performance, and accelerate growth across Global Commercial Services (GCS). Working closely with GCS senior leadership and cross-functional teams across Marketing, Finance, Product, Technology, and Capabilities, the role applies advanced data science and analytics to solve complex business challenges and influence strategic decisions. Focus areas include identifying growth opportunities, understanding Sales and client behaviors, optimizing incentive effectiveness, and identifying emerging performance risks. The role leads the development and application of advanced statistical and machine learning models, scalable analytical solutions, and GenAI-powered capabilities using large language models, direct API integrations, and unstructured data. It also leads a team of data scientists, develops relationships with stakeholders, and communicates analytical findings and initiative progress to executive and technical audiences. This is a fast-paced position requiring relationship skills, applied data science expertise, technical leadership, and a commitment to excellence.

Requirements

At least 5 years of relevant work experience in data science, machine learning, and advanced analytics.
Bachelor’s degree, preferably in a quantitative field such as Economics, Finance, Computer Science, Mathematics, Statistics, or Engineering.
Experience developing and deploying statistical, machine learning, and deep learning models to solve business problems using tools such as scikit-learn, PyTorch, or TensorFlow.
Experience developing GenAI solutions, including LLM API integration, retrieval-augmented generation (RAG), and model evaluation.
Strong analytical and conceptual thinking skills, with the ability to translate complex business problems into quantitative approaches and practical solutions.
Proficiency in Python and SQL, with experience processing and analyzing large-scale structured and unstructured datasets using distributed computing frameworks such as Spark.
Ability to synthesize complex analytical findings, develop effective data visualizations, and influence decisions across senior leadership and cross-functional stakeholders.
Ability to work independently and collaboratively across teams, demonstrating problem-solving skills, intellectual curiosity, and adaptability.

Nice To Haves

Master’s degree or PhD in a quantitative field, such as Computer Science, Statistics, Econometrics, Mathematics, Physics, Operations Research, or Engineering.
Executive-level stakeholder management experience.
People leadership experience.

Responsibilities

Design, develop, and deploy predictive and explanatory solutions using machine learning, NLP, and generative AI to address business problems, strengthen forecasting, refine incentive plan designs, and identify gaming behaviors.
Deliver high-impact analytics and actionable insights into Sales and client behavior, introducing approaches to transform complex behavioral data and influence organizational decision-making.
Lead workstreams to design, develop, and operationalize a GenAI-enabled analytics solution that synthesizes internal performance data and external competitive signals into actionable insights, with defined success metrics and ongoing monitoring.
Build and evaluate models using modern machine learning frameworks, focusing on scalability, performance, and interpretability.
Lead a team of high-performing data scientists.
Establish and maintain relationships with cross-functional stakeholders to understand business strategies, develop goals, and address opportunities.
Architect and deploy robust, efficient, and scalable data pipelines and modeling solutions using modern cloud and distributed compute patterns.
Stay current on machine learning, deep learning, and generative AI; evaluate emerging approaches and translate them into practical, scalable solutions that improve business outcomes.
Lead analytics and measurement of key performance indicators and own stakeholder and executive communications on initiative progress, including automated monthly measurement tied to strategic initiatives.
Present findings, recommendations, and results to technical and non-technical audiences, including executive leadership, using clear reports, visualizations, and presentations.