Nomura Services India Data Consultant Interview: Rounds, Questions and Preparation

What the job involves

Data Consultants in GCCs, consulting firms and the Big 4 work with client teams to translate business problems into data-driven solutions. They clean and analyse data, build dashboards and models, and present findings to stakeholders. The role blends technical data work with client communication and project delivery.

Role and Technical Knowledge

1. 1

   How would you explain the difference between a data analyst and a data consultant to a non-technical client?

   Highlight that a consultant not only analyses data but also advises on business strategy, communicates insights, and manages client relationships.

2. 2

   What are the key steps you would follow to clean a messy dataset before analysis?

   Mention steps like handling missing values, removing duplicates, standardising formats, outlier detection, and documenting assumptions.

3. 3

   In SQL, how would you find the second highest salary from an employee table?

   Use a subquery with LIMIT/OFFSET or a window function like DENSE_RANK(), and explain the logic clearly.

4. 4

   What is the difference between supervised and unsupervised learning? Give a business example of each.

   Define both, then give examples like predicting customer churn (supervised) and customer segmentation (unsupervised).

Practical Scenarios

1. 5

   A client in retail wants to reduce inventory costs but fears stockouts. How would you use data to help them?

   Discuss demand forecasting, ABC analysis, safety stock optimisation, and creating a dashboard to monitor key metrics.

2. 6

   You are given sales data for a bank's credit cards. How would you identify which customer segments are most profitable?

   Outline segmentation by demographics, spending patterns, and profitability metrics like revenue per customer and lifetime value.

3. 7

   Your manager asks you to create a dashboard for a telecom client to track customer complaints. What metrics would you include and why?

   Include metrics like complaint volume, resolution time, first-call resolution rate, and customer satisfaction scores, and explain their business impact.

4. 8

   How would you handle a situation where the data you received from a client is incomplete or inconsistent?

   Describe a structured approach: communicate with the client, document issues, impute or exclude data carefully, and validate with stakeholders.

Behavioural and HR

1. 9

   Why do you want to work as a Data Consultant in a GCC or consulting firm like Nomura Services India?

   Show awareness of the sector, mention exposure to diverse clients, and align your skills with the role's blend of analytics and consulting.

2. 10

   Describe a time when you had to explain a complex technical concept to someone without a technical background.

   Use the STAR method, focus on simplifying jargon, using analogies, and checking for understanding.

3. 11

   Tell me about a project where you worked in a team. What was your role and how did you handle disagreements?

   Highlight collaboration, active listening, and how you resolved conflicts to achieve the project goal.

4. 12

   Where do you see yourself in five years in this role?

   Show ambition to grow into a lead consultant or subject matter expert, and mention continuous learning and client impact.

Case Study and Problem Solving

1. 13

   A hotel chain is seeing a decline in bookings. How would you structure your analysis to identify the root cause?

   Break down into internal and external factors, analyse booking data by channel, seasonality, competitor pricing, and customer feedback.

2. 14

   You are asked to estimate the market size for electric vehicles in India. How would you approach this?

   Use a top-down or bottom-up approach, segment by vehicle type, region, and adoption rate, and state assumptions clearly.

3. 15

   A client wants to improve customer retention. What data would you ask for and what analysis would you perform?

   Request customer demographics, transaction history, and engagement data; then perform cohort analysis, churn prediction, and lifetime value analysis.

4. 16

   How would you measure the success of a marketing campaign for a retail client?

   Define KPIs like ROI, conversion rate, customer acquisition cost, and incremental sales, and suggest A/B testing or control groups.