American Express
Senior Manager - Data Science
New York, NY · Hybrid · Manager · Full-time · Posted Oct 28, 2026
- Experience
- 5+ years of relevant work experience in data science, machine learning, and advanced analytics.
- Education
- Bachelor’s degree, preferably in a quantitative field.
- Visa sponsorship
- American Express may provide visa sponsorship for certain positions, depending on factors such as business unit requirements, the nature of the position, cost, and applicable laws.
About the job
Leads data science and analytics initiatives to improve sales effectiveness, portfolio performance, and growth across Global Commercial Services, including developing scalable predictive and GenAI-enabled solutions.
What you'll do
- Lead development and deployment of predictive, statistical, machine learning, and GenAI analytics solutions for business priorities.
- Lead a team of data scientists, collaborate with stakeholders, and communicate insights and initiative performance to executives.
- Build scalable data pipelines and modeling solutions, and define measurement and monitoring for strategic initiatives.
What they ask for
- Experience developing and deploying statistical, machine learning, and deep learning models for business problems.
- Experience developing GenAI solutions, including LLM API integration, retrieval-augmented generation (RAG), and model evaluation.
- Proficiency in Python and SQL, with experience analyzing large-scale structured and unstructured data using distributed computing frameworks.
- Ability to translate complex business problems into quantitative solutions and communicate findings to senior leaders and cross-functional stakeholders.
Nice to have
- Master’s degree or PhD in a quantitative field.
- Executive-level stakeholder management or people leadership experience.
Pay
$123000 - $215250 annually USD (base salary); bonus + benefits
Tools
scikit-learn, PyTorch, TensorFlow, Python, SQL, Spark, LLMs
AI focus
Develop and operationalize GenAI analytics solutions using LLMs, API integrations, unstructured data, and RAG; develop and evaluate machine learning, deep learning, and NLP models.
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