Senior Analyst-Data Science

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 Senior 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.

Shift Time- 1:30 PM - 9:30 PM IST

- Degree in a quantitative field preferred, such as Engineering, Mathematics, Computer Science, Finance, Economics, Statistics, or a related discipline.
- 2+ 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 suchas 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 management, and evaluation frameworks.
- Strong analytical and problem-solving skills, with the ability to execute well-defined analytical tasks accurately and efficiently.
- Demonstrated ability to manage assigned work independently while collaborating effectively within a cross-functional team.
- High attention to detail, intellectual curiosity, and an experimentation mindset with the ability to evaluate solutions objectively and iterate based on evidence.
- Strong written and verbal communication skills, with the ability to clearly explain analytical findings and support stakeholder discussions.
- Familiarity with Responsible AI principles, including explainability, bias and fairness assessment, hallucination mitigation, prompt safety, model monitoring, evaluation, audit readiness, and GenAI risk management practices.