What is the difference between WHERE and HAVING in SQL, and when would you use each?

Explain that WHERE filters rows before aggregation, HAVING filters after GROUP BY. Give a practical example like filtering sales above a threshold after grouping by region.

How would you handle missing values in a dataset for a client project?

Discuss options like deletion, mean/median imputation, or predictive filling, and mention the need to understand why data is missing before deciding.

Explain a p-value and how you would use it in an A/B test for a client.

Define p-value as the probability of observing results if the null hypothesis is true. Explain that a low p-value (e.g., <0.05) suggests a significant effect, but also mention practical significance.

What steps would you take to ensure data quality before building a dashboard?

Cover profiling, checking for duplicates, outliers, consistency, and validating against source systems. Mention documenting assumptions and cleaning steps.

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GCCs, consulting and the Big 4

Raytheon Technologies India Data Consultant Interview: Rounds, Questions and Preparation

For Data Consultant roles at companies like Raytheon Technologies India

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https://www.cynchrony.in/library/raytheon-technologies-india-data-consultant

What the job involves

A Data Consultant in GCCs, consulting, and Big 4 firms helps clients clean, analyse, and visualise data to solve business problems. Day to day, you gather requirements, write SQL and Python scripts, build dashboards, and present insights to stakeholders. You often work on multiple projects, ensuring data quality and translating technical findings into clear business recommendations.

Role and Technical Knowledge

1
What is the difference between WHERE and HAVING in SQL, and when would you use each?
Explain that WHERE filters rows before aggregation, HAVING filters after GROUP BY. Give a practical example like filtering sales above a threshold after grouping by region.
2
How would you handle missing values in a dataset for a client project?
Discuss options like deletion, mean/median imputation, or predictive filling, and mention the need to understand why data is missing before deciding.
3
Explain a p-value and how you would use it in an A/B test for a client.
Define p-value as the probability of observing results if the null hypothesis is true. Explain that a low p-value (e.g., <0.05) suggests a significant effect, but also mention practical significance.
4
What steps would you take to ensure data quality before building a dashboard?
Cover profiling, checking for duplicates, outliers, consistency, and validating against source systems. Mention documenting assumptions and cleaning steps.

Practical Scenarios

5
A client says their sales dashboard shows incorrect numbers. How would you troubleshoot?
Start by reproducing the issue, checking data refresh, filters, and joins. Communicate with the client to understand expected values and verify against raw data.
6
You have to analyse customer churn for a telecom client. What data would you request and what approach would you take?
Ask for customer demographics, usage patterns, complaints, and contract details. Suggest exploratory analysis, cohort analysis, and maybe a logistic regression to identify drivers.
7
You are given a large dataset with 50 columns and asked to find key drivers of revenue. How would you proceed?
Start with data cleaning, then use correlation analysis, feature importance from models, or dimension reduction. Focus on business relevance and avoid overfitting.
8
How would you explain a complex statistical finding to a non-technical client?
Use analogies, simple language, and visualisations. Focus on the 'so what' - the business impact - rather than the technical details.

Behavioural and HR

9
Why do you want to work as a Data Consultant in a GCC or Big 4 firm?
Show understanding of the consulting lifestyle, variety of projects, and client exposure. Align your skills and career goals with the firm's focus on data-driven solutions.
10
Describe a time you worked in a team to solve a problem. What was your role?
Use the STAR method. Highlight collaboration, your specific contribution, and the outcome. Emphasise learning and adaptability.
11
Tell me about a time you had to meet a tight deadline. How did you manage?
Show prioritisation, time management, and communication. Mention tools like to-do lists or project management software, and the result.
12
Where do you see yourself in five years?
Focus on growing into a subject matter expert or leading projects, contributing to business growth. Show commitment to learning and the consulting path.

Sector and Company Fit

13
What do you understand about the consulting industry and how data consultants add value?
Talk about solving client problems, improving efficiency, and enabling data-driven decisions. Mention the project-based nature and need for strong communication.
14
How would you handle a situation where a client disagrees with your analysis?
Stay calm, listen to their concerns, and re-examine your work. Use data to support your findings but be open to alternative interpretations and collaborate on a solution.
15
What trends in data analytics do you think are important for consulting firms?
Mention AI/ML, cloud analytics, real-time dashboards, and data privacy. Relate them to how consulting firms can help clients adopt these.
16
Why should we hire you over other candidates?
Summarise your unique blend of technical skills, business acumen, and communication. Show enthusiasm and fit with the firm's culture and values.

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This is practice material for Data Consultant roles, written with AI. It is not a list of questions Raytheon Technologies India or any other company has asked.