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REQUIREMENT_ID_158 β€’ 3-DAY_ACTIVE_POLICY

Analyst – Data Science

Company
Company American Express
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary 12 LPA (industry competitive)
Location
Location Bangalore, Karnataka
Posted Date
Posted Date Yesterday
Python SQL Google Cloud Platform Vertex AI BigQuery PySpark Hadoop Machine Learning Data Modeling JIRA Rally Confluence Jupyter Notebook Airflow Product Management
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Aptitude Practice Questions Open Resource β†—
Curated aptitude questions to sharpen logical and quantitative reasoning for the online assessment.
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Company Interview Preparation Guide Open Resource β†—
Comprehensive guide covering common interview patterns and technical topics relevant to American Express data roles.
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Interview Preparation Resources Open Resource β†—
A collection of study notes, mock tests, and interview experiences to help candidates prepare for technical and HR rounds.
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Coding Practice Platform Open Resource β†—
Extensive problem set for practicing coding questions in Python and SQL, essential for the technical assessment.

Eligibility Criteria: - Educational Qualification: B.E/B.Tech/B.Sc or M.E/M.Tech/M.Sc in Computer Science, Information Technology, Mathematics or related fields. - Minimum Academic Performance: 60% aggregate (or CGPA equivalent) in the qualifying degree. - Batch: 2026 (freshers) or graduating in 2025‑2026. - Backlog Policy: No active backlogs at the time of joining; a maximum of 2 backlogs allowed in the final year, provided they are cleared before the joining date. - Technical Prerequisites: Strong foundation in AI/ML, Python, SQL, and hands‑on experience with GCP (Vertex AI, BigQuery) and Big Data tools (PySpark/Hadoop).

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 (AI product case study)
5
Round 5: HR Interview (behavioral and cultural fit)
American Express is a globally integrated payments and financial services company that has built a reputation for innovation, data‑driven decision making and a customer‑centric culture. With a presence in over 130 countries, Amex combines cutting‑edge technology with deep financial expertise to deliver products and experiences that enrich lives. In India, the firm is expanding its analytics and AI capabilities, offering fresh talent the chance to work on high‑impact projects that influence millions of transactions daily. The company’s work environment emphasizes continuous learning, mentorship, and a collaborative spirit, making it an attractive destination for ambitious graduates. The role of Analyst – Data Science in the Bangalore office is a hybrid position that blends core data‑science engineering with AI product management. As a fresher, you will be part of the Data Science product team, responsible for turning business problems into scalable AI solutions on Google Cloud Platform. You will not only develop machine‑learning models but also help shape the product roadmap, prioritize features, and ensure that prototypes evolve into production‑grade services. Key Responsibilities: 1. Contribute to the definition and articulation of long‑term AI product strategy and measurable business metrics. 2. Prioritize and manage product backlogs using JIRA/Rally, ensuring alignment with stakeholder expectations. 3. Design, develop, and validate end‑to‑end ML models, from data ingestion to feature engineering and model training. 4. Deploy models on GCP services such as Vertex AI and monitor performance in real‑time. 5. Create proof‑of‑concepts (POCs) for innovative AI‑ML products with scalability in mind. 6. Collaborate closely with engineering, UX, and data‑engineering teams to transition MVPs into production‑ready solutions. 7. Conduct market and competitor research to inform product enhancements and roadmap decisions. 8. Document model lifecycle processes, including data lineage, versioning, and compliance requirements. 9. Participate in code reviews, knowledge‑sharing sessions, and continuous improvement initiatives. 10. Assist in preparing technical and business presentations for senior leadership. Tech Stack: Python, SQL, PySpark, Hadoop, Google Cloud Platform (BigQuery, Vertex AI), Jupyter notebooks, Airflow, Git, JIRA/Rally, Confluence. Growth 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 specialized roles such as ML Engineer, Solutions Architect, or Business Analytics Manager. Why Join American Express? The company offers a best‑in‑industry compensation package, exposure to global financial data, and a culture that values curiosity and innovation. Freshers get mentorship from seasoned data scientists, access to world‑class cloud infrastructure, and the chance to see their models impact real‑world financial products. The blend of technical depth and product ownership makes this role a unique launchpad for a career in AI and analytics.

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, Data Modeling, JIRA, Rally, Confluence, Jupyter Notebook, Airflow, Product Management) & 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?
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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