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REQUIREMENT_ID_2118 • 3-DAY_ACTIVE_POLICY JD

Data Science Associate

Company
Company Accenture
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary 9.4 - 19.4 LPA
Location
Location Bengaluru, Karnataka; Kolkata, West Bengal; Gurugram, Haryana
Posted Date
Posted Date Today
Apply on Official Website ↗ 🤝 Connect Recruiter ↗
ME
Meera Sengupta ✓ Verified Recruiter
Recruitment Lead • Advanced Technology Centers in India (ATCI)
🏢 Accenture • Direct Recruiter Email: meera.sengupta@accenture.com
Python SQL Generative AI Prompt Engineering APIs Retrieval‑Augmented Generation (RAG) Agentic AI LangChain LangGraph Machine Learning Azure AWS
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Comprehensive Placement Paper Collection Open Resource ↗
Curated set of Accenture placement papers that help candidates practice the types of questions asked in the assessment and technical rounds.
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Interview Experience Insights Open Resource ↗
First‑hand accounts of Accenture recruitment experiences, useful for understanding interview flow and preparing relevant answers.
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Accenture Recruitment Preparation Guide Open Resource ↗
A structured guide covering aptitude, coding, and AI‑focused topics commonly tested for the Decision Science Practitioner role.
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Algorithm Practice Problem Set Open Resource ↗
Extensive library of coding problems to sharpen algorithmic thinking and Python proficiency required for the technical assessment.

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Statistics, Applied Mathematics, Engineering or a related discipline; minimum 60% aggregate (or equivalent CGPA); graduating batch 2025‑2027; up to 2 years of relevant experience allowed; no active backlogs at the time of joining.

1
Round 1: Online Assessment (aptitude & coding)
2
Round 2: Technical Interview (Python, SQL, AI concepts, problem solving)
3
Round 3: HR Interview (fit, motivations, behavioral questions)
Accenture is a global professional services powerhouse that blends consulting, technology, and operations to help clients navigate digital transformation. With a presence in more than 120 countries, the firm is known for its strong focus on innovation, sustainability, and inclusive culture. Employees benefit from exposure to cutting‑edge projects across industries, robust learning platforms, and a collaborative environment that encourages continuous upskilling. Accenture’s AI and analytics practice, part of the Strategy & Consulting (S&C) Global Network – AI – Hi Tech division, drives next‑generation solutions using generative AI, large language models, and advanced data science techniques. The Decision Science Practitioner Associate role is designed for early‑career talent eager to work at the intersection of data science and generative AI. As a Data Science Associate, you will support the design, development, and validation of LLM‑powered applications, retrieval‑augmented generation (RAG) pipelines, and conversational agents. The position offers hands‑on experience with prompt engineering, API integration, and cloud AI services, while collaborating with senior practitioners to deliver client‑focused solutions. Key Responsibilities: 1. Assist in building and testing LLM‑driven applications using Python, APIs, and prompt‑engineering best practices. 2. Contribute to RAG workflows: curate knowledge sources, generate embeddings, and integrate retrieval mechanisms. 3. Develop and fine‑tune conversational AI agents, including tool‑calling and multi‑step orchestration. 4. Perform functional testing of AI outputs, create test cases, and document validation results. 5. Analyze response quality, identify hallucinations or inconsistencies, and suggest improvements. 6. Support deployment and monitoring of AI services on Azure or AWS platforms. 7. Write clean, reusable Python code and manage version control using Git. 8. Participate in code reviews, documentation, and knowledge‑sharing sessions. 9. Collaborate with cross‑functional teams to translate business problems into technical solutions. 10. Stay updated on emerging AI trends, frameworks (LangChain, LangGraph), and best practices. Tech Stack: Python, SQL, REST APIs, Git, Docker basics, Azure/AWS AI services, LangChain, LangGraph, scikit‑learn, TensorFlow/PyTorch (optional), vector databases. Growth Path: Starting as an Associate, high performers can progress to Decision Science Practitioner, Senior Associate, Manager, and eventually to leadership roles within Accenture’s AI practice. The firm offers structured learning pathways, certifications, and mentorship programs to accelerate career growth. Why Join Accenture? You will work on real‑world AI projects for Fortune‑500 clients, gain exposure to the latest generative AI technologies, and benefit from a culture that values diversity, continuous learning, and work‑life balance. The role provides a solid foundation for a long‑term career in data science and AI engineering.

Accenture — 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 🎯
Accenture Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Accenture 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, Generative AI, Prompt Engineering, APIs, Retrieval‑Augmented Generation (RAG), Agentic AI, LangChain, LangGraph, Machine Learning, Azure, 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
Accenture 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 Accenture as a Data Science Associate?
Preparation Tip: Highlight Accenture'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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