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Data Science Analyst
American Express. Contribute to analytical solutions, predictive models, recommendation and decisioning approaches that improve servicing experiences, personalization, colleague effectiveness, and operational outcomes .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and evaluating machine learning and GenAI solutions, with a strong foundation in Python and SQL. Capable of translating complex analytical findings into actionable insights while fostering cross-functional collaboration.
Highest-signal resume keywords
Machine Learning Model DevelopmentPython ProficiencySQL ProficiencyAnalytical Problem SolvingCommunication Skills
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningData ScienceAdvanced AnalyticsStatistical AnalysisFeature EngineeringModel EvaluationPredictive ModelingRegression ModelsClustering TechniquesExperiment Design
Soft Skills
Analytical ThinkingCollaborationCommunicationCuriosityAdaptability
Tools & Technologies
GenAIConversational AITransformer ArchitecturesRecommendation SystemsDecision Science SolutionsMLOpsLLMOps
Certifications & Qualifications
Master’s Degree in Quantitative Field
Industry Keywords
Agentic AIPersonalizationNext-Best-ActionCustomer DecisioningBehavioral Analysis
Tech Stack
Tools & technologiesPythonSQL
About the role
Key responsibilities & impact- Contribute to analytical solutions, predictive models, recommendation and decisioning approaches that improve servicing experiences, personalization, colleague effectiveness, and operational outcomes
- Explore and analyze customer, behavioral, interaction, and operational datasets to identify patterns and opportunities
- Engineer features, test hypotheses, and translate findings into actionable insights and solutions
- Develop and evaluate machine learning and GenAI solutions using modeling, experimentation, and validation techniques
- Contribute to Agentic AI and agentic workflows, transformer-based recommendation systems, Conversational AI/LLM applications, and advanced personalization or next-best-action solutions
- Support experiment design and analysis and performance measurement for model/AI quality, business impact, and improvement opportunities
- Write high-quality analytical code and work with data, Technology, and platform partners on implementation, productionization, monitoring, and enhancement of Decision Science solutions
- Build cross-functional relationships, communicate analytical findings clearly, and translate technical results into insights and recommendations
- Build knowledge of advances in machine learning, GenAI, recommendation systems, and decision intelligence
- Test relevant methods and contribute to reusable approaches and best practices across GSDS
Requirements
What you’ll need- Master’s degree in a quantitative field (e.g., Engineering, Computer Science, Mathematics, Statistics, Economics, Finance)
- Relevant academic, internship, or professional experience in Data Science, Machine Learning, Advanced Analytics, or a related quantitative field
- Proficiency in Python, SQL, or similar analytical tools
- Foundational experience building machine learning models such as tree-based models, regression/classification models, clustering, or related techniques
- Exposure to or coursework/project experience with LLMs, GenAI, or modern deep-learning approaches
- Strong analytical and conceptual thinking with the ability to structure problems, learn quickly, and work through ambiguity
- Strong written and verbal communication skills and the ability to work effectively in collaborative, cross-functional teams
- Preferred: Exposure to Agentic AI/agentic frameworks, transformer architectures, transformer-based recommendation systems, Conversational AI, LLM applications, embeddings, or semantic modeling
- Preferred: Exposure to personalization, recommendation systems, next-best-action, optimization, experimentation, or customer decisioning problems
- Preferred: Experience or project exposure working with structured or unstructured datasets and developing analytical or machine learning solutions
- Preferred: Familiarity with model/AI evaluation and basic concepts related to productionization, monitoring, or MLOps/LLMOps
- Preferred: Curiosity about customer servicing and ability to connect technical work to measurable customer and business outcomes
- Successful completion of a background verification check, subject to applicable laws and regulations
Benefits
Comp & perks- Competitive base salaries
- Bonus incentives
- Support for financial well-being and retirement
- Comprehensive medical, dental, vision, life insurance, and disability benefits (depending on location)
- Flexible working model with hybrid, onsite or virtual arrangements depending on role and business need
- Generous paid parental leave policies (depending on your location)
- Free access to global on-site wellness centers staffed with nurses and doctors (depending on location)
- Free and confidential counseling support through our Healthy Minds program
- Career development and training opportunities