American Express—Analyst-Data Analytics

Job description

Description: You Lead the Way. We’ve Got Your Back. At American Express, we know that with the right backing, people and businesses have the power to progress in incredible ways. Whether we’re supporting our customers’ financial confidence to move ahead, taking commerce to new heights, or encouraging people to explore the world, our colleagues are constantly redefining what’s possible, and we’re proud to back each other every step of the way. When you join #TeamAmex, you become part of a diverse community with a common goal to deliver an exceptional customer experience every day. We back our colleagues with the support they need to thrive, professionally and personally. That’s why we have Amex Flex, our enterprise working model that provides greater flexibility to colleagues while ensuring we preserve the important aspects of our unique in-person culture. Depending on role and business needs, colleagues will either work onsite or in a hybrid model. Global Merchant & Network Services (GMNS), the merchant and bank partner network of American Express, acquires and maintains relationships with merchants and banks who welcome American Express branded cards. GMNS Product & GNPS Scaled Analytics Team aims to provide best-in-class scaled analytics & products to support key GMNS priorities and strategic initiatives, optimize investments, accelerate merchant and network activation, and enhance the value American Express acceptance brings to merchants and Card Members at scale. Requirements: Experience - 1-3 years of relevant experience in analytics, data engineering, business intelligence, data science, or analytical product development. - Hands-on experience using Python and SQL to analyze and transform complex datasets and deliver business insights. - Practical experience with Tableau and/or Power BI for dashboarding, visualization, and business performance reporting. - Exposure to cloud-based data platforms, CI/CD and modern data engineering practices particularly in environments using Google Cloud Platform or similar products will be preferred. - Working familiarity with GenAI, prompt engineering, model-enabled applications, agentic workflow concepts, or AI-led analytics/product prototypes will be preferred. - Prior experience in network business, payments, merchant services, financial services, or a related domain will be helpful. Education - Bachelor’s degree in Statistics, Mathematics, Economics, Computer Science, Data Science, Engineering, or a related quantitative discipline. - Preferred: Post-graduate qualification in Statistics, Mathematics, Economics, Computer Science, Data Science, Engineering, or Management. Technical Skills - Proficiency in Python and SQL, with hands-on knowledge of Pandas, NumPy, and Streamlit. - Working knowledge of data pipelines, data modeling, data transformation, and large-scale data handling concepts; exposure to Hive, PySpark, Big Data technologies, distributed systems, or Airflow-based workflow orchestration will be an added advantage. - Hands-on experience with Tableau and/or Power BI, including dashboard design, visualization best practices, semantic metrics, performance optimization, and BI automation. - Strong working knowledge of MS Office, particularly Advanced Excel and PowerPoint. - Familiarity with Google Cloud Platform services such as BigQuery, Cloud Storage, Dataproc, Dataflow, Cloud Run, Dataplex, and Vertex AI will be preferred. - Foundational understanding of prompt engineering, structured prompting, retrieval-augmented generation (RAG), embeddings and vector search, tool calling, agentic workflows, evaluation, guardrails, and responsible AI will be an added advantage. - Familiarity with Git, APIs, predictive modeling, AI/ML algorithms, and analytical product prototyping will be helpful. Core Competencies - Strong analytical and problem-solving skills with the ability to derive insights from complex data. - Innovative thinker with strong problem-solving skills and ability to challenge the status quo to drive impactful change. - Proven ability to manage multiple projects and deliver results in fast-paced environments. - Innovative problem solver: quick learner who can work independently on unstructured initiatives. - Effective collaborator with strong relationship-building skills. - Excellent business communication and presentation abilities, with experience influencing strategy at all levels of management. Responsibilities: Business Outcomes: - Frame ambiguous business problems, develop hypotheses, and translate complex analysis into clear, actionable recommendations for GMNS and network partner priorities. - Deliver scalable analytical products, BI solutions, and insight frameworks that improve decision-making, strengthen partner performance visibility, and support strategic business outcomes. - Apply analytics, automation, and modern data practices to simplify recurring workflows, improve speed to insight, and enable repeatable capability development. - Work with large and complex datasets using tools such as Python, SQL, Big Data platforms, Google Cloud Services, and visualization platforms to generate reliable, high-quality insights. - Build strong measurement, KPI, segmentation, trend, root-cause, reconciliation, and opportunity-sizing approaches that help identify drivers, risks, anomalies, and growth opportunities. - Embed data quality, governance, documentation, and responsible AI principles into analytical products and processes to improve trust, consistency, and enterprise readiness. - Partner with business, product, engineering, and regional stakeholders to shape use cases, recommend practical solutions, and drive adoption of data-backed decisions at scale. Leadership Outcomes: - Bring enterprise-first thinking to the role, connecting analytics priorities to broader GMNS goals while balancing the needs of customers, partners, colleagues, and shareholders. - Demonstrate ownership, sound judgment, and learning agility while independently navigating ambiguous, evolving, and cross-functional initiatives. - Challenge the status quo constructively, identify opportunities for innovation, and recommend pragmatic improvements that can scale across products, processes, and teams. - Build trusted relationships across business, product, engineering, and analytics teams, influencing stakeholders through clear communication, strong business storytelling, and transparent articulation of risks and trade-offs. - Operate with integrity, attention to detail, and a commitment to responsible, well-governed analytical outcomes.