Opportunity brief
About Trent Limited
Trent Limited, a part of the Tata Group, is one of India's leading retail companies, operating iconic brands such as Westside and Zudio. With a rapidly expanding retail footprint, Trent leverages data and technology to power critical business decisions across merchandising, customer insights, inventory optimization, and operational excellence.
Job Description: Data Scientist
Function: AI & Data Science | Location: Mumbai | Employment Type: Full-time
Role Overview
As a Data Scientist, you will be part of the Data Science and AI team, turning retail data into actionable insights and drive change through technology. You will support data analysis, machine learning development, GenAI experimentation, and stakeholder communication. You will get an opportunity to work with structured data, text, documents, and images across several strategic business use cases.
Key Responsibilities
- Support the development of machine learning models for forecasting, classification, clustering, and recommendations.
- Perform exploratory data analysis to identify trends, patterns, and anomalies.
- Transform and validate structured and unstructured data, including text and images.
- Work on AI and GenAI use cases such as summarization, information extraction, document processing, and image analysis.
- Generate intelligent insights and communicate using visualizations
- Collaborate with stakeholders and document methods, assumptions, and results.
Knowledge & Skills
- Foundation in statistics, probability, and machine learning concepts.
- Hands-on knowledge of Python, Pandas, NumPy, and scikit-learn.
- Working knowledge of SQL for data extraction and manipulation.
- Basic understanding of GenAI concepts such as LLMs, prompting, embeddings, vector search, and RAG.
- Awareness of techniques for working with text and image data.
- Ability to communicate insights to both technical and non-technical stakeholders.
Good to Have
- Exposure to Azure, Databricks or other cloud platforms.
- Exposure to building GenAI applications using LLM APIs, Azure OpenAI, LangChain, or similar tools.
- Awareness of evaluating GenAI outputs for relevance, accuracy, quality, and hallucinations.
- Basic knowledge of NLP, computer vision, deep learning, or time-series forecasting.
- Familiarity with Git and collaborative development practices.
- Familiarity with Power BI or similar visualization tools.
Education
- Bachelor's / Master's degree in Computer Science, Statistics, Mathematics, Data Science, Economics or a related quantitative discipline.
What We Offer
- Opportunity to work on high-impact problems in a fast-paced retail environment.
- Structured mentorship, guided learning pathways, and opportunities to learn from experienced team members and leaders.
- Hands-on exposure to modern analytics, data science, and AI tools.
- Career growth opportunities through cross-functional projects and real-world problem-solving.
Work Schedule
- 5 day working week; 2nd and 4th Saturdays are working days.
Recruitment Process
The recruitment process consists of the following stages:
- Aptitude and Coding Round: Complete a structured online assessment featuring aptitude, technical, and coding questions to evaluate your analytical thinking, technical knowledge, and coding proficiency.
- Applied Data Science Round: Work on a business case and submit a structured solution to demonstrate your problem-solving ability, analytical thinking, and business acumen.
- Virtual Interview Round: Participate in a live technical interview to showcase your technical expertise, problem-solving approach, and ability to apply concepts to real-world scenarios.
- In Person Interview Round: Engage in a live discussion with the hiring team to assess your communication skills, professional experience, career aspirations, and overall fit for the role.
- Offer Rollout: Candidates who successfully clear all stages of the recruitment process will receive an offer from Trent.
Please note that candidates shortlisted in the Aptitude and Coding Round must complete the Applied Data Science Round within 72 hours for their submission to be considered valid.