Analyst - Digital Product Analytics

About the role

Collects, analyzes, and reports product data to support decisions on day-to-day operations, strategic planning/forecasting and business issues/insights. Applies quantitative analysis, data mining and other advanced analytics techniques to better understand how users interact with products. Recommends which product data and metrics to monitor and measure to produce relevant business insights, including new and alternative data sources.

Responsibilities

• Assists in developing data reports and dashboards to support product stakeholders and collaborators.
• Supports product teams with statistical and quantitative analysis.
• Works with business subject matter experts for Data Analysis on leveraging the expertise on Portfolio Metrics, Agility Metrics and Productivity Metrics.
• Works with disparate datasets to prepare reporting or analytical models.
• Sums up results to address identified product team needs.
• Translates complex technical concepts and analysis to non-technical audiences.
• Works with product subject matter experts to define data and analytics requirements.
• Contribute to project plans, use cases, user stories, or business cases.
• Covers for the execution of the Data Analytics plan.
• Convert Data into Insights with Data Analytical Skills using GEN AI Toolsets available within Amex.
• Participates in Program Increment (PI) meetings and ensures the Stories and Features are planned in the backlog.
• Understands and applies Artificial Intelligence, Machine Learning, BI and Visualization trends, and market standard methodologies.

Qualifications

• Skilled in reporting/analytical applications (Python, Power BI, Data Modelling, and strong Excel Skills).
• With hands-on Python skills, understands and applies Artificial Intelligence, Machine Learning Models (Regression and Classification), Business Intelligence and Visualization trends, and market standard methodologies.
• Experience in using LLMs (OpenAI GPT Models and Claude Code), Retrieval Augmented Generation (RAG), LangChain and Vectorization Techniques (TFIDF, Glove, BGE and Open AI Embeddings).
• Knowledge on implementing Agentic AI use cases will be Plus.