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REQUIREMENT_ID_64 β€’ 3-DAY_ACTIVE_POLICY

Software Engineer (Data & AI)

Company
Company Merckgroup
Type
Opportunity Type Internship
Salary
Stipend / Salary 12-15 LPA
Location
Location India
Posted Date
Posted Date Today
Python PySpark Spark Snowflake Palantir Foundry AWS CI/CD Git Docker Data Engineering AI/ML fundamentals Data Governance SQL REST APIs
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Aptitude and Reasoning Practice Open Resource β†—
Helps candidates sharpen quantitative and logical reasoning skills essential for Merck's online assessment.
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Company Interview Preparation Hub Open Resource β†—
Provides curated interview experiences, sample questions and tips that are useful for Merck's technical rounds.
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Comprehensive Interview Guides Open Resource β†—
Offers detailed guides on data engineering, AI concepts and behavioral interview strategies relevant to Merck roles.
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Algorithm Practice Platform Open Resource β†—
Enables practice of coding problems on data structures and algorithms, a core component of Merck's technical assessment.

Bachelor's or Master's degree in Computer Science, Information Technology, Electronics, or related engineering discipline; minimum 60% aggregate (or CGPA 6.0/10); graduating batch 2024‑2026; no active backlogs at the time of joining; strong academic record in programming, data structures and algorithms; fluency in English (both written and spoken).

1
Round 1: Online coding assessment (data structures, algorithms, SQL)
2
Round 2: Technical interview (system design, data pipelines, AI concepts)
3
Round 3: HR interview (cultural fit, motivations, compensation discussion)
Merck Group, a global leader in science and technology, has a legacy of more than 350 years in delivering innovative solutions across Healthcare, Life Science, and Electronics. With a presence in over 60 countries, the company combines deep scientific expertise with cutting‑edge digital capabilities to improve the lives of patients, customers, and communities worldwide. In India, Merck operates state‑of‑the‑art research centres, manufacturing hubs and a growing digital ecosystem that supports its mission of "Sparking Discovery and Elevating Humanity". The culture emphasizes curiosity, inclusion, and a relentless focus on ethical innovation, making it an attractive destination for fresh talent eager to make a tangible impact. The role of Software Engineer (Data & AI) sits within the Enabling Functions Data Office, a product‑oriented delivery unit that builds data‑driven platforms for HR, procurement, legal and other core functions. As a member of an internationally distributed pod, you will translate complex business problems into scalable data pipelines, AI‑enabled services and intuitive user interfaces. You will own end‑to‑end delivery – from architecture and coding to testing, deployment and monitoring – while ensuring compliance with governance, security and cost‑efficiency standards. Key Responsibilities: 1. Design, develop and maintain robust data pipelines using PySpark, Snowflake and Palantir Foundry. 2. Build AI‑powered micro‑services that automate manual workflows across Enabling Functions. 3. Implement CI/CD pipelines and automated testing frameworks for both data and AI components. 4. Ensure data lineage, governance, privacy and security controls are embedded in all solutions. 5. Collaborate with product owners, business analysts and domain experts to capture requirements and translate them into technical specifications. 6. Optimize cloud resources on AWS to achieve cost‑effective scalability. 7. Participate in code reviews, knowledge‑sharing sessions and mentorship of junior engineers. 8. Contribute to the evolution of the data engineering practice, defining standards and best practices. 9. Support the Spain‑based hub by coordinating cross‑regional deliverables and aligning on global AI use‑cases. 10. Stay abreast of emerging AI/ML technologies and propose innovative use‑cases such as carbon‑footprint analytics and procurement intelligence. Tech Stack: Python, PySpark, Spark, Snowflake, Palantir Foundry, AWS (S3, Lambda, Glue), Git, Docker, Kubernetes, CI/CD tools (Jenkins/GitHub Actions), MLOps basics. Growth Path: Starting as a Software Engineer, you can progress to Senior Engineer, Lead Engineer, and eventually Data Platform Architect or AI Product Manager, with opportunities to move across functional domains or geographies. Why Join Merck? You will work on enterprise‑scale AI projects that directly influence the efficiency of a multinational corporation, gain exposure to world‑class data platforms, and be part of a culture that values scientific rigor, ethical AI, and personal development. The role offers a blend of technical challenge, global collaboration, and a purpose‑driven mission that resonates with ambitious fresh graduates.

Merckgroup 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 🎯
Merckgroup Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Merckgroup 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, PySpark, Spark, Snowflake, Palantir Foundry, AWS, CI/CD, Git, Docker, Data Engineering, AI/ML fundamentals, Data Governance, SQL, REST APIs) & 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
Merckgroup 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 Merckgroup as a Software Engineer (Data & AI)?
Preparation Tip: Highlight Merckgroup'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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