Manager-Data Science

American Express
New York
Workplace: HybridFull timeUSD 103,750 - 174,750 annuallyFunction: Data Science & Machine LearningExperience: 2+ yearsEducation: bachelorsSkills: ["Communication","Stakeholder management","Executive presentation","Cross-functional collaboration","Business problem solving"]

Lead analytical initiatives for the next generation of corporate customer acquisition across Sales & Marketing, combining advanced analytics, machine learning, and Generative AI. Own end-to-end solution delivery—from problem definition through deployment—building production-ready data products with Marketing, Sales, Product, Digital, and Engineering. Drive experimentation and optimization using A/B testing and model monitoring, ensure Responsible AI governance, and present actionable insights to senior leadership.

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FursaFursa
American Express
American Express
1 day ago

Manager-Data Science

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Job Summary

Lead analytical initiatives for the next generation of corporate customer acquisition across Sales & Marketing, combining advanced analytics, machine learning, and Generative AI. Own end-to-end solution delivery—from problem definition through deployment—building production-ready data products with Marketing, Sales, Product, Digital, and Engineering. Drive experimentation and optimization using A/B testing and model monitoring, ensure Responsible AI governance, and present actionable insights to senior leadership.
Location: New York
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Manager level

Key Responsibilities

  • •Translate complex business problems into structured analytical frameworks and data-driven solutions to improve targeting, personalization, and acquisition performance.
  • •Design, build, and deploy predictive models, ML solutions, customer scoring/prioritization frameworks, and AI-powered analytical products.
  • •Develop scalable analytical pipelines using Python and SQL with production readiness, automation, and reusable components.
  • •Apply Generative AI techniques (LLMs, RAG, embeddings, prompt engineering, NLP) to enhance customer acquisition and decision intelligence.
  • •Lead experimentation and optimization (A/B tests, impact measurement, and model performance monitoring) while ensuring Responsible AI principles and presenting results to senior leadership.

Pay and Benefits

Salary: USD 103,750 - 174,750 annually
Perks:Health InsuranceDentalVisionLife InsuranceDisability Benefits401k MatchingParental LeaveFinancial CoachingCounseling SupportWellness Stipend

Key Requirements

  • •Bachelor’s degree in a quantitative discipline (Engineering, Computer Science, Statistics, Mathematics, Economics, Finance, or related).
  • •2+ years of experience in Data Science, Advanced Analytics, Machine Learning, Decision Science, or Customer Analytics.
  • •Hands-on experience with Python and SQL to build end-to-end analytical solutions on large-scale datasets.
  • •Experience developing predictive models and machine learning solutions for customer targeting or decisioning frameworks that drive measurable outcomes.
  • •Working knowledge of Generative AI concepts and applications, including LLMs, prompt engineering, RAG, or NLP techniques.
Experience:2+ years
Education:Bachelor's
Skills:CommunicationStakeholder managementExecutive presentationCross-functional collaborationBusiness problem solving
Languages:US
Tech Stack:PythonSQLMachine LearningGenerative AILLMsPrompt engineeringRAGNLPEmbeddingsA/B testingCausal measurementModel performance monitoring

Company Brief

American Express
Provides global payment, credit, and travel-related financial services for consumers and businesses. Best known for its charge and credit cards, merchant payment network, and premium customer rewards and servicing.
Industry: Payments
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: New York City, United States
Founded: 1850
WebsiteLinkedIn