Analyst-Data Analytics

At American Express, delivering exceptional customer experiences is central to building lasting customer relationships and driving sustainable business growth. Understanding what customers experience, why those experiences occur, and how they influence customer behavior enables us to identify opportunities to improve satisfaction, strengthen loyalty and retention, and drive better business and operational outcomes.

The Customer Listening – Insights & Analytics team brings together customer feedback, interaction, servicing, and operational data to generate insights that shape customer experience (CX) strategy and business decisions.

We are looking for an analytically strong and intellectually curious analyst who can combine deep data and statistical understanding, strong business judgment, and structured problem solving to solve complex CX problems.

This is not a reporting-focused role. The successful candidate will be expected to take ambiguous business questions, translate them into testable hypotheses, determine the right data and analytical approach, and turn evidence into clear business recommendations. The role also requires hands-on application of text analytics, NLP, GenAI and other advanced analytical techniques to derive insights from structured and unstructured customer data.

• Solve complex CX and business problems end-to-end – structure ambiguous questions, develop hypotheses, identify relevant data, select appropriate analytical methods, interpret results, and recommend actions.
• Own and deliver CX analytics across servicing channels
• Develop executive-ready insight packages and narratives to support decision-making
• Drive cross-channel customer journey analysis to identify friction points and improvement opportunities
• Apply sample vs population-based methodologies to ensure scalable and statistically sound insights
• Leverage NLP techniques to extract customer sentiment, themes, and unmet needs
• Integrate Gen AI / LLM-based workflows to enhance analytics efficiency and innovation
• Partner with senior stakeholders to shape analytics roadmaps and align with business priorities
• Bring in external best practices in CX and analytics to continuously elevate team output
• Ensure timely, accurate, and impactful delivery across all analytics initiatives

Required Qualifications and Skills

• Strong foundation in statistics and data reasoning, including practical understanding of population vs. sample, sampling and selection bias, distributions, confidence intervals, hypothesis testing, correlation vs. causation and regression.
• Strong analytical-method selection, with working knowledge of commonly used data-mining and machine-learning techniques such as segmentation, clustering, classification and regression, including when to use them, their underlying assumptions and how to evaluate results.
• Strong logical and structured problem-solving ability, with demonstrated ability to break ambiguous business problems into hypotheses, determine the evidence required, evaluate alternative explanations and arrive at well-supported conclusions.
• Strong SQL skills and proficiency in Python for data manipulation, statistical analysis, automation and analytical model development.
• Strong written, verbal and visual communication skills, with the ability to simplify complex analytical findings and influence business stakeholders through evidence.
• Demonstrated intellectual curiosity, attention to detail and willingness to challenge assumptions rather than accepting results at face value.

Preferred Skills

• Experience with customer experience and behavior analytics to drive business strategies.
• Experience with Tableau, Power BI or other data-visualization platforms.
• Experience translating analytical findings into recommendations that have influenced customer, operational or business outcomes.
• Hands-on experience with text analytics, NLP, GenAI or related advanced analytical techniques, with an understanding of how these approaches can be applied to extract meaningful insights from large volumes of unstructured customer data.

Education

• Bachelor’s degree in Statistics, Mathematics, Economics, Operations Research, Engineering, Computer Science, Data Science, Business Analytics, or another quantitative discipline.
• Postgraduate degree in a quantitative or analytical discipline is preferred but not required.
• Relevant certifications in statistics, data science, machine learning, NLP or advanced analytics are a plus.