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REQUIREMENT_ID_1338 • 3-DAY_ACTIVE_POLICY

Data Scientist

Company
Company Trent Limited
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary 6 LPA - 9 LPA (depending on experience and skill set)
Location
Location Mumbai
Posted Date
Posted Date Sep 25, 2026
Python SQL Statistics Machine Learning Data Visualization Problem Solving Communication Business Acumen Git AWS
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Aptitude Practice Questions Open Resource ↗
Helps you sharpen quantitative and logical reasoning skills essential for the problem‑solving assessment.
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Technical Interview Preparation Open Resource ↗
Covers core computer‑science concepts and coding patterns frequently asked in data‑science interviews.
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Trent Limited Data Scientist Interview Guide Open Resource ↗
Provides role‑specific insights, sample questions, and tips tailored to Trent’s data‑science hiring process.
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Algorithm & Data Structure Problem Set Open Resource ↗
Offers a wide range of coding challenges to improve problem‑solving speed and accuracy for the coding portion of the assessment.

Graduates (B.Tech/B.E., B.Sc., M.Tech, M.Sc.) in Computer Science, Data Science, Statistics, Mathematics, Information Technology or related fields. Minimum 60% aggregate (or CGPA 6.0/10) in the qualifying degree. No active backlogs at the time of joining. Final year students can apply if they can join within 2 months of graduation. Preference for batches 2023‑2025, but open to all freshers and experienced candidates.

1
Round 1: Data Science & Problem‑Solving Assessment
2
Round 2: Virtual Interview Round
3
Round 3: In‑Person Interview Round
4
Round 4: Offer Rollout
Trent Limited, a flagship company of the Tata Group, has carved a niche in Indian retail with iconic brands such as Westside, Zudio, and Star Bazaar. With over 300 stores across the country, Trent combines fashion, lifestyle, and technology to deliver a seamless shopping experience. The organization’s data‑driven culture fuels decisions in merchandising, inventory management, customer insights, and operational efficiency, making it a fertile ground for analytics talent. As the retail landscape becomes increasingly competitive, Trent’s AI & Data Science function is expanding rapidly to harness the power of big data, machine learning, and generative AI. The Data Scientist role sits at the heart of this transformation. Reporting to the Head of AI & Data Science, you will work closely with product managers, merchandising teams, and engineering to translate massive retail datasets into actionable insights. Your day‑to‑day responsibilities will include exploratory data analysis, building predictive models for demand forecasting, customer segmentation, and recommendation systems, as well as experimenting with GenAI tools to automate reporting and insight generation. You will also be expected to communicate findings to non‑technical stakeholders, ensuring that data‑driven recommendations are understood and implemented. **Key Responsibilities** 1. Perform end‑to‑end data analysis on sales, inventory, and customer behavior data. 2. Develop, validate, and deploy machine‑learning models for demand forecasting, price optimization, and personalized recommendations. 3. Conduct A/B tests and evaluate model performance using appropriate statistical metrics. 4. Collaborate with data engineers to design robust data pipelines and ensure data quality. 5. Experiment with Generative AI for automated report generation and insight extraction. 6. Create dashboards and visualizations that simplify complex insights for business users. 7. Document methodologies, code, and model versioning for reproducibility. 8. Stay updated with the latest research in retail analytics, deep learning, and NLP. 9. Mentor junior analysts and contribute to a culture of continuous learning. 10. Participate in cross‑functional workshops to identify new analytics opportunities. **Tech Stack**: Python (pandas, scikit‑learn, TensorFlow/PyTorch), SQL, Spark, Tableau/PowerBI, Git, Docker, AWS (S3, EC2, SageMaker), and emerging GenAI platforms. **Growth Path**: Starting as a Data Scientist, high performers can progress to Senior Data Scientist, Lead Data Scientist, and eventually Head of AI & Data Science, with opportunities to lead large‑scale analytics projects across the entire Trent ecosystem. **Why Join Trent**: You will be part of a forward‑thinking retail giant that values innovation, offers exposure to real‑world, high‑volume data, and provides a collaborative environment backed by the Tata Group’s strong ethical foundation. The role promises a blend of technical depth and business impact, making it an ideal launchpad for a data‑science career in India’s booming retail sector.

Trent Limited — Data Analytics & SQL Interview Guide

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

APTITUDE & LOGIC 📖
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 ↗
COMPANY GUIDE 🎯
Trent Limited Interview Preparation Corner

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

Open Resource ↗
MOCK TESTS & PAPERS 📝
Comprehensive Interview Prep Resources & Syllabus

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

Open Resource ↗
CODING PRACTICE 💻
Algorithm and Data Structure Problem Set

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

Open Resource ↗
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, Statistics, Machine Learning, Data Visualization, Problem Solving, Communication, Business Acumen, Git, AWS) & 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
Trent Limited 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 ▼
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 ▼
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 ▼
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 ▼
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 ▼
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 Trent Limited as a Data Scientist?
Preparation Tip: Highlight Trent Limited'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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