Analyst – Data Science

American Express

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2 weeks ago

Job Description

For the American Express Recruitment 2026 drive, the company is seeking an Analyst to join the Data Science product team. This role is a unique blend of Technical Data Science and AI Product Management . You will not only build ML models but also contribute to the long-term AI product strategy and roadmap.
You will work with Google Cloud Platform (GCP) and Big Data tools to translate business requirements into technical solutions. The role involves driving end-to-end ML/AI product developments, transforming MVPs into production-grade capabilities, and collaborating with engineering and UX teams.

As an Analyst – Data Science at American Express, your key responsibilities will include:
Product Strategy: Contributing to the defining and articulation of long-term AI product strategy and roadmaps with clearly defined business metrics.
Backlog Management: Prioritizing and managing product backlogs using tools like JIRA and Rally .
Model Development: Driving end-to-end ML/AI product developments, from feature selection to deployment.
Lifecycle Management: Contributing to all product lifecycle processes including market research, roadmap development, and requirements finalization.
Innovation: Creating POCs (Proof of Concepts) for best-in-class AI-ML innovative products with scaling potential.
Collaboration: Collaborating with engineering and design teams to transform MVPs into production-grade capabilities.

To be eligible for American Express Recruitment 2026, candidates must meet the following criteria:
Educational Background: Undergraduate or Master’s in Computer Science, Information Technology, or Mathematics from institutes of global repute.
Mandatory Technical Skills: Strong background in AI / ML with proficiency in Python and SQL . Knowledge of Google Cloud Platform (GCP) , BigQuery, and Vertex AI. Familiarity with Big Data Platforms like Hadoop and PySpark. Understanding of the ML Model Development Lifecycle (MDLC), including decision trees and boosting algorithms.
Strong background in AI / ML with proficiency in Python and SQL .
Knowledge of Google Cloud Platform (GCP) , BigQuery, and Vertex AI.
Familiarity with Big Data Platforms like Hadoop and PySpark.
Understanding of the ML Model Development Lifecycle (MDLC), including decision trees and boosting algorithms.
Tools & Frameworks: Knowledge of Notebook-based IDEs (Jupyter) and Airflow. Familiarity with product management tools like Rally, JIRA, and Confluence .
Knowledge of Notebook-based IDEs (Jupyter) and Airflow.
Familiarity with product management tools like Rally, JIRA, and Confluence .
Soft Skills: Strong quantitative and structured problem-solving skills with excellent communication abilities.

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