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Data Analyst

Accenture

  • Bangalore, Gurgaon, Hyderabad, Mumbai
  • 6-9 LPA
  • 2-5 Years
  • Posted Today
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About the role

Accenture is a global professional services company with leading capabilities in digital, cloud and security. With more than 700,000 people serving clients in over 120 countries, Accenture helps organizations transform and stay ahead of disruption. The Strategy & Consulting arm, especially the Global Network – Data & AI practice, partners with Fortune‑500 firms to turn data into actionable insights, enabling smarter decisions across functions such as finance, marketing, and supply chain.

The practice is known for its collaborative culture, continuous learning programs, and a strong focus on innovation through AI, machine learning and advanced analytics.

The role of Ind & Func AI Decision Science Analyst sits at the intersection of data science and supply‑chain management. As part of the Supply Chain Analytics team, you will work on end‑to‑end analytical solutions that help clients forecast demand, optimise inventory, and improve logistics efficiency. You will translate complex business problems into data‑driven models, communicate insights to senior stakeholders, and contribute to the development of reusable analytical assets for the practice.

Key Responsibilities

  • Design and implement statistical and machine‑learning models for demand forecasting, inventory optimisation and network design.
  • Conduct time‑series analysis and develop predictive algorithms to anticipate supply‑chain disruptions.
  • Translate client business requirements into analytical solutions, preparing clear documentation and visualisations.
  • Collaborate with cross‑functional teams (operations, IT, finance) to integrate analytics into existing business processes.
  • Perform data extraction, cleaning and transformation using SQL, Python or R.
  • Validate model performance, conduct sensitivity analysis and recommend improvements.
  • Create dashboards and interactive reports using Power BI/Tableau to convey insights.
  • Participate in asset development initiatives, building reusable code libraries and best‑practice templates.
  • Stay updated with emerging AI/ML techniques and propose innovative applications for supply‑chain challenges.
  • Support client workshops and presentations, articulating technical concepts in business language.

Tech Stack: Python, R, SQL, Power BI/Tableau, Azure ML, Spark, statistical packages (statsmodels, scikit‑learn), optimization libraries (PuLP, Gurobi).

Growth Path: Analysts typically progress to Senior Analyst, Consultant, Manager and eventually to Principal/Managing Director, with opportunities to lead large‑scale transformation programs and specialize in industry verticals.

Why Join Accenture? You will work on high‑impact projects for global brands, gain exposure to cutting‑edge AI tools, and benefit from a culture that values diversity, mentorship, and continuous upskilling. The firm’s strong emphasis on employee development, flexible work arrangements, and robust performance‑linked rewards make it an attractive destination for ambitious data professionals.

Eligibility & process

Who can apply

Graduation (B.Tech/B.E/M.Tech/M.Sc) in Computer Science, Statistics, Operations Research, Industrial Engineering or related fields; Minimum 60% aggregate (or CGPA 6.5/10); No active backlogs (max 2 allowed at the time of application); Experience of 2-5 years in analytics, data science or supply‑chain consulting; Strong academic record and demonstrated problem‑solving ability.

Skills & stack

Statistics Time Series Forecasting Optimization Machine Learning Python SQL Data Visualization Power BI

Selection process

  1. Round 1: Online Assessment (aptitude + technical coding)
  2. Round 2: Technical Interview (case studies, analytics concepts, tools)
  3. Round 3: HR Interview (fit, motivation, cultural alignment)

Prep kit

20 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
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 (Statistics, Time Series Forecasting, Optimization, Machine Learning, Python, SQL, Data Visualization, Power BI) & 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.
Technical questions 5
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.
Behavioural & HR questions 3
Why do you want to join Accenture as a Data Analyst?
Highlight Accenture's market reputation, recent tech innovations, and how your skills in Statistics directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
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?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Accenture 6-9 LPA
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