
Required Skills & Tech Stack
Python
SQL
Google Cloud Platform
Vertex AI
BigQuery
PySpark
Hadoop
Machine Learning
Decision Trees
XGBoost
JIRA
Rally
Confluence
Jupyter
Airflow
Product Management
Communication
Problem Solving

Eligibility Criteria
Undergraduate (B.E/B.Tech/B.Sc) or Postβgraduate (M.E/M.Tech/M.Sc) degree in Computer Science, Information Technology, Mathematics or related fields from a recognized institute. Minimum aggregate of 60% (or CGPA 6.0/10). Freshers from the 2026 batch are eligible. No active backlogs at the time of joining. Candidates must possess strong fundamentals in AI/ML, Python, SQL and GCP.

Job Description & Key Responsibilities
American Express is a globally integrated payments and financial services company that has built a reputation for dataβdriven decision making. With a presence in more than 130 countries, Amex combines cuttingβedge technology with deep financial expertise to deliver products, insights and experiences that enrich lives. The companyβs culture emphasizes innovation, collaboration and continuous learning, making it an attractive destination for fresh talent eager to work on realβworld problems at scale.\n\nThe Data Science Analyst role in Bangalore sits at the intersection of technical AI/ML development and product management. As a fresher, you will be part of a highβimpact team that builds endβtoβend machineβlearning solutions on Google Cloud Platform, while also shaping the longβterm AI product roadmap. This hybrid position offers the chance to write productionβgrade code, design experiments, and translate business requirements into scalable AI products.\n\nKey Responsibilities:\n1. Contribute to the definition and articulation of AI product strategy and roadmaps aligned with business metrics.\n2. Prioritize, groom and manage product backlogs using JIRA/Rally.\n3. Design, develop, and validate machineβlearning models from feature engineering to deployment on Vertex AI.\n4. Build proofβofβconcepts (POCs) for innovative AI/ML solutions with scaling potential.\n5. Collaborate with engineering, UX and data engineering teams to turn MVPs into productionβgrade capabilities.\n6. Own the endβtoβend ML lifecycle, including data ingestion, model training, monitoring and continuous improvement.\n7. Conduct market and competitor research to inform product enhancements.\n8. Document technical designs, experiment results and product specifications in Confluence.\n9. Participate in code reviews and knowledgeβsharing sessions to foster a learning environment.\n10. Communicate findings and product impact to stakeholders across business and technology functions.\n\nTech Stack: Python, SQL, Google Cloud Platform (BigQuery, Vertex AI), PySpark, Hadoop, Jupyter notebooks, Airflow, JIRA, Rally, Confluence, Decision Trees, XGBoost and other ML algorithms.\n\nGrowth Path: Starting as an Analyst β Data Science, high performers can progress to Senior Analyst, AI Product Manager, or Data Science Lead within 2β3 years, with opportunities to move into broader product or engineering leadership roles.\n\nWhy Join? You will work with a worldβclass data science team, gain handsβon experience with GCPβs AI services, and influence product decisions that affect millions of customers. The role offers a blend of coding depth and strategic product exposure, excellent mentorship, and a compensation package that is among the best in the industry.