The AIM (Analytics, Investment & Marketing Enablement) team – a part of GCS Marketing – is the analytical engine that enables the Global Commercial Services portfolio of American Express. Accelerating growth momentum, increasing profitability, and strengthening our value proposition are key objectives for this organization.
This Analyst – Data Science role, based out of India, will join the Prospect Cross-functional team within AIM and execute analytical workstreams supporting prospect targeting and acquisition initiatives. Leveraging a broad analytical toolkit—including advanced machine learning, predictive modeling, optimization, and Generative AI—the role supports the end-to-end development of scalable analytical solutions that enhance targeting precision, engagement effectiveness, and marketing ROI.
This role provides an opportunity to build next-generation AI-powered capabilities across prospect enrichment, intelligent targeting, lead prioritization, and decision support by combining advanced analytics with modern Generative AI techniques. Working closely with data scientists, product managers, engineers, and business stakeholders, the Analyst will develop scalable analytical solutions that accelerate data-driven decision making while maintaining the highest standards of Responsible AI and model governance.
Responsibilities
Execute analytical and data science solutions to solve business problems using statistical techniques, machine learning, and modern Generative AI approaches.
Develop, test, and maintain analytical models and AI-enabled data products—including predictive models, prospect scoring, prioritization, matching, enrichment, and intelligent decision-support capabilities—to improve targeting and acquisition effectiveness.
Design, build, and evaluate Generative AI workflows, including Retrieval-Augmented Generation (RAG), embedding-based retrieval, semantic search, prompt engineering, structured outputs, and LLM-powered applications, selecting appropriate approaches based on business requirements, solution quality, scalability, latency, cost, and governance considerations.
Design and execute experiments to evaluate prompting strategies, retrieval configurations, model selection, and workflow architectures, continuously optimizing solution quality, response accuracy, operational efficiency, and business impact.
Perform data extraction, preparation, feature engineering, and data quality validation using large-scale datasets to support AI/ML model development and analytical initiatives.
Apply statistical and machine learning techniques to improve model performance through experimentation, feature refinement, validation, and continuous model optimization using established best practices.
Collaborate with product, engineering, and business stakeholders to support the implementation, deployment, monitoring, and continuous improvement of analytical and Generative AI solutions within business workflows.
Evaluate AI and GenAI solution performance using quantitative and qualitative evaluation techniques, including model performance metrics, retrieval quality, response accuracy, prompt robustness, business outcome measures, and production monitoring.
Communicate analytical findings, model outputs, and business insights clearly through presentations and technical documentation for both technical and non-technical stakeholders.
Ensure compliance with Responsible AI principles, model governance, data integrity, explainability, bias assessment, prompt safety, monitoring, audit readiness, and enterprise risk management standards throughout the analytical development lifecycle.
Qualifications
Degree in a quantitative field preferred, such as Engineering, Mathematics, Computer Science, Finance, Economics, Statistics, or a related discipline.
1+ years of experience in data science, advanced analytics, machine learning, decision science, or related quantitative roles.
Strong programming skills in Python and SQL, with working knowledge of Hive and/or PySpark in large-scale data environments, hands-on experience developing machine learning models end-to-end, and familiarity with software engineering best practices including Git-based version control.
Demonstrated hands-on experience building or experimenting with LLM-based applications, including Retrieval-Augmented Generation (RAG), prompt engineering, semantic search, embeddings, structured outputs, or AI-powered assistants.
Knowledge of supervised machine learning techniques (e.g., gradient boosting, tree-based models, regression, clustering) and statistical techniques such as hypothesis testing, multivariate testing, ANOVA, and model evaluation methodologies.
Exposure to AI agent frameworks, tool/function calling, vector databases, LLM orchestration frameworks (e.g., LangChain, LlamaIndex, DSPy), context management, or workflow automation is a plus.
Familiarity with modern ML/AI development frameworks, open-source libraries, prompt lifecycle