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REQUIREMENT_ID_337 β€’ 3-DAY_ACTIVE_POLICY

Analyst – Data Science (AI/ML)

Company
Company American Express
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary 5 - 10 LPA
Location
Location Bangalore, Karnataka
Posted Date
Posted Date Today
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
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Placement Papers for Oracle Open Resource β†—
Curated past placement papers that help you practice the type of questions asked in Oracle recruitment drives.
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Recruitment Process Experiences Open Resource β†—
First‑hand experiences from candidates who have cleared Oracle's recruitment process, useful for interview preparation.
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Interview Preparation Guide Open Resource β†—
Comprehensive guide covering topics, sample questions and tips specifically for Oracle interviews.
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Comprehensive Coding Problem Set Open Resource β†—
Extensive collection of coding problems to sharpen algorithmic skills required for technical assessments.

Undergraduate (B.E/B.Tech/B.Sc) or Post‑graduate (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.

1
Round 1: Resume Screening
2
Round 2: Online Technical Assessment (Python/SQL coding + aptitude)
3
Round 3: Technical Interview 1 (ML algorithms, GCP, SQL)
4
Round 4: Technical Interview 2 (Product case study)
5
Round 5: HR Interview
American Express is a globally integrated payments and financial services company that has built a reputation for data‑driven decision making. With a presence in more than 130 countries, Amex combines cutting‑edge technology with deep financial expertise to deliver products, insights and experiences that enrich lives. The company’s culture emphasizes innovation, collaboration and continuous learning, making it an attractive destination for fresh talent eager to work on real‑world 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 high‑impact team that builds end‑to‑end machine‑learning solutions on Google Cloud Platform, while also shaping the long‑term AI product roadmap. This hybrid position offers the chance to write production‑grade 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 machine‑learning models from feature engineering to deployment on Vertex AI.\n4. Build proof‑of‑concepts (POCs) for innovative AI/ML solutions with scaling potential.\n5. Collaborate with engineering, UX and data engineering teams to turn MVPs into production‑grade capabilities.\n6. Own the end‑to‑end 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 knowledge‑sharing 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 – Data Science, high performers can progress to Senior Analyst, AI Product Manager, or Data Science Lead within 2‑3 years, with opportunities to move into broader product or engineering leadership roles.\n\nWhy Join? You will work with a world‑class data science team, gain hands‑on experience with GCP’s 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.

American Express β€” Data Analytics & SQL Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC πŸ“–
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

Open Resource β†—
COMPANY GUIDE 🎯
American Express Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for American Express and off-campus tech roles.

Open Resource β†—
MOCK TESTS & PAPERS πŸ“
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource β†—
CODING PRACTICE πŸ’»
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource β†—
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core 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) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
American Express Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the difference between WHERE and HAVING clauses in SQL? Answer β–Ό
Model Answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer β–Ό
Model Answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer β–Ό
Model Answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer β–Ό
Model Answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer β–Ό
Model Answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
Why do you want to join American Express as a Analyst – Data Science (AI/ML)?
Preparation Tip: Highlight American Express's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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