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Accenture Data Science Consultant Interview Questions, Process, and Tips

Updated 10 Dec 2024

Top Accenture Data Science Consultant Interview Questions and Answers

View all 6 questions

Accenture Data Science Consultant Interview Experiences

8 interviews found

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
-
Result
-

I applied via Campus Placement

Round 1 - Technical 

(2 Questions)

  • Q1. Design a dwmand planning system
  • Ans. 

    Design a demand planning system for efficient forecasting and inventory management.

    • Utilize historical sales data to identify trends and seasonality

    • Incorporate external factors like market trends, promotions, and competitor activities

    • Implement machine learning algorithms for accurate demand forecasting

    • Integrate with inventory management systems for optimized stock levels

    • Regularly review and adjust the system based on pe

  • Answered by AI
  • Q2. Pick one point from resume and explain
  • Ans. 

    Implemented machine learning model to predict customer churn for a telecom company

    • Developed and trained a machine learning model using Python and scikit-learn

    • Utilized historical customer data to identify patterns and factors leading to churn

    • Evaluated model performance using metrics such as accuracy, precision, and recall

    • Provided actionable insights to the telecom company based on the model's predictions

  • Answered by AI

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Coding Test 

Python Coding and ML concepts

Round 2 - One-on-one 

(2 Questions)

  • Q1. How would you support a colleague who has made a mistake.
  • Ans. 

    Support colleague by offering assistance, understanding, and constructive feedback.

    • Offer emotional support and reassurance to help alleviate any stress or anxiety.

    • Provide constructive feedback in a respectful and non-judgmental manner.

    • Offer assistance in rectifying the mistake and finding a solution together.

    • Encourage open communication and a growth mindset to learn from the mistake.

    • Avoid blaming or shaming the colleag

  • Answered by AI
  • Q2. Case study of a short analysis

Data Science Consultant Interview Questions Asked at Other Companies

asked in Deloitte
Q1. Why did you choose those specific technologies/algorithms?
asked in Accenture
Q2. Guest Estimates on how many sofa sold in a day in your city
asked in Accenture
Q3. How do you approach an end to end ML problem
asked in Deloitte
Q4. Explain KDD and explain each step in detail.
asked in Accenture
Q5. Difference between boosting and bagging techniques ?
Interview experience
3
Average
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • Q1. Guest Estimates on how many sofa sold in a day in your city
  • Ans. 

    It is difficult to estimate the exact number of sofas sold in a day in a city without specific data.

    • The number of sofas sold in a day can vary greatly depending on factors such as population size, economic conditions, and consumer preferences.

    • One way to estimate could be to look at the number of furniture stores in the city and their average daily sales.

    • Another approach could be to conduct a survey of a sample of furni...

  • Answered by AI
  • Q2. Ml algorithm’s terminology
  • Ans. 

    ML algorithm's terminology refers to the specific vocabulary used to describe concepts, processes, and components in machine learning models.

    • Supervised learning: algorithms learn from labeled training data, e.g. linear regression, support vector machines

    • Unsupervised learning: algorithms find patterns in unlabeled data, e.g. clustering, dimensionality reduction

    • Feature engineering: process of selecting, transforming, and...

  • Answered by AI

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Referral and was interviewed in Dec 2023. There were 2 interview rounds.

Round 1 - Technical 

(1 Question)

  • Q1. Difference between boosting and bagging techniques ?
  • Ans. 

    Boosting and bagging are ensemble learning techniques used to improve the performance of machine learning models.

    • Boosting focuses on improving the performance of a single model by training multiple models sequentially, where each subsequent model corrects the errors of its predecessor.

    • Bagging, on the other hand, involves training multiple models independently and then combining their predictions through averaging or vo...

  • Answered by AI
Round 2 - One-on-one 

(1 Question)

  • Q1. Questions based on projects in resume

Skills evaluated in this interview

Accenture interview questions for designations

 Data Science Engineer

 (2)

 Data Science Intern

 (1)

 Data Science Analyst

 (11)

 Data Science Manager

 (3)

 Data Science Lead

 (1)

 Data Scientist

 (31)

 Senior Data Analyst

 (11)

 Senior Data Scientist

 (6)

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
Selected Selected

I applied via LinkedIn and was interviewed before Dec 2023. There were 2 interview rounds.

Round 1 - Technical 

(3 Questions)

  • Q1. How do you approach an end to end ML problem
  • Ans. 

    I approach an end to end ML problem by understanding the problem, collecting data, preprocessing data, selecting a model, training the model, evaluating the model, and deploying the model.

    • Understand the problem and define the objective

    • Collect and preprocess data

    • Select a suitable machine learning model

    • Train the model using the data

    • Evaluate the model's performance

    • Deploy the model for production use

  • Answered by AI
  • Q2. Basics of Statistics
  • Q3. ML algorithms evaluation techniques
  • Ans. 

    Evaluation techniques for machine learning algorithms include cross-validation, confusion matrix, ROC curve, and precision-recall curve.

    • Cross-validation: Splitting the data into multiple subsets for training and testing to assess model performance.

    • Confusion matrix: A table showing the true positive, true negative, false positive, and false negative predictions of a model.

    • ROC curve: Receiver Operating Characteristic cur...

  • Answered by AI
Round 2 - Technical 

(2 Questions)

  • Q1. Questions on NLP and word embeddings
  • Q2. Questions on my past project experience

Interview Preparation Tips

Interview preparation tips for other job seekers - Interview was conducted in 2021. It was not hard.

Skills evaluated in this interview

Get interview-ready with Top Accenture Interview Questions

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
4-6 weeks
Result
Selected Selected

I applied via Company Website and was interviewed before Apr 2023. There were 2 interview rounds.

Round 1 - Technical 

(1 Question)

  • Q1. Drilled through statistics, machine learning, deep learning along with few questions on sql, python. Touched upon cloud concepts.
Round 2 - Behavioral 

(1 Question)

  • Q1. Scenario based question. Project discussion

Data Science Consultant Jobs at Accenture

View all
Interview experience
5
Excellent
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Approached by Company and was interviewed before Oct 2022. There were 4 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all tips
Round 2 - HR 

(1 Question)

  • Q1. Basic resume screening
Round 3 - Technical 

(1 Question)

  • Q1. NLP case study on a oil company
Round 4 - Case Study 

Devise a strategy for a coffee retail chain who is planning to enter a new market

Data Science Consultant Interview Questions & Answers

user image Chinmay Tamhankar

posted on 30 Oct 2023

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
Selected Selected

I applied via Recruitment Consulltant and was interviewed before Oct 2022. There were 4 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Don’t add your photo or details such as gender, age, and address in your resume. These details do not add any value.
View all tips
Round 2 - HR 

(1 Question)

  • Q1. Salary expectations, role fitment
Round 3 - Case Study 

Case study, past experience

Round 4 - Behavioral 

(1 Question)

  • Q1. Expertise, expectations, ambition

Interview questions from similar companies

I applied via Referral and was interviewed before Apr 2020. There were 3 interview rounds.

Interview Questionnaire 

1 Question

  • Q1. Technical questions on eloqua, email marketing

Interview Preparation Tips

Interview preparation tips for other job seekers - Be comfident while answering

I applied via Approached by Company and was interviewed before May 2021. There were 4 interview rounds.

Round 1 - Coding Test 

Online questions were based on scenarios to write SQL queries. Also got few questions on Python as well for which I had only limited knowledge.

Round 2 - Technical 

(8 Questions)

  • Q1. I'm listing some of the questions here which the interviewers (2 panel resources) asked me regarding Informatica ETL, Oracle DB and I might have missed some of them. What types of indexes are used on Orac...
  • Q2. What happens if Treat source row property as "Update" at session level but at target object "Delete" is checked?
  • Ans. 

    The source row will be treated as an update, but the target object will be deleted.

    • The session level property 'Treat source row as Update' will be applied to the source row.

    • The target object will be deleted regardless of the update status of the source row.

    • This can result in data loss if the source row contains important information.

  • Answered by AI
  • Q3. What are the different types of dimension tables?
  • Ans. 

    Dimension tables are used in data warehousing to provide descriptive information about the data in fact tables.

    • Slowly changing dimensions

    • Junk dimensions

    • Degenerate dimensions

    • Role-playing dimensions

    • Bridge dimensions

  • Answered by AI
  • Q4. What schema type was used in your previous project? And explain why it was used?
  • Ans. 

    We used a relational schema in our previous project as it was suitable for the data structure and allowed for efficient querying.

    • Relational schema was used as it allowed for efficient querying of data

    • The data structure was suitable for a relational schema

    • We were able to easily join tables to retrieve necessary data

    • Examples include using SQL to query a database with multiple tables

    • Normalization was used to reduce data r

  • Answered by AI
  • Q5. Difference between Joiner and Lookup transformations?
  • Ans. 

    Joiner combines data from multiple sources based on a common key, while Lookup retrieves data from a reference table based on a matching key.

    • Joiner is used to combine data from two or more sources based on a common key column.

    • Lookup is used to retrieve data from a reference table based on a matching key column.

    • Joiner can perform inner, outer, left, and right joins, while Lookup can only perform an inner join.

    • Joiner can...

  • Answered by AI
  • Q6. Scenario: I have records in a flat file ending with ; character at the end of each them. How to load these records into DB based on this character? What property will be used in Informatica?
  • Q7. How version control happened in your previous project?
  • Ans. 

    We used Git for version control in our previous project.

    • We created a Git repository for the project.

    • All team members were added as collaborators to the repository.

    • We followed the Git flow branching model.

    • We used pull requests for code review and merging.

    • We used tags to mark important releases.

    • We regularly pushed our changes to the remote repository.

    • We used Git commands like commit, push, pull, merge, and rebase.

    • We used...

  • Answered by AI
  • Q8. Which SCD type you worked on before and explain on that?
  • Ans. 

    I have worked on SCD Type 2 before.

    • SCD Type 2 is used to track historical changes in data.

    • It creates a new record for each change and maintains a history of changes.

    • It includes start and end dates for each record.

    • Example: Tracking changes in employee salary over time.

  • Answered by AI
Round 3 - One-on-one 

(1 Question)

  • Q1. This round was taken by the project manager on a phone call. He asked me few basic fundamental questions related to my skills and the things which I worked on in my previous project. He also told me about ...
Round 4 - HR 

(1 Question)

  • Q1. Just F2F discussion with HR finalising on the compensation structure, learning opportunities and the organisational benefits as an employee.

Interview Preparation Tips

Interview preparation tips for other job seekers - If you're an experienced person, be confident about the things which you handled in previous project and be precise on to the point. Don't elaborate too much as the interviewers might build up questions based on that. Be strong in your technical areas of expertise. Expect scenario based questions related to your skills.

Skills evaluated in this interview

Accenture Interview FAQs

How many rounds are there in Accenture Data Science Consultant interview?
Accenture interview process usually has 2-3 rounds. The most common rounds in the Accenture interview process are Technical, HR and Case Study.
How to prepare for Accenture Data Science Consultant interview?
Go through your CV in detail and study all the technologies mentioned in your CV. Prepare at least two technologies or languages in depth if you are appearing for a technical interview at Accenture. The most common topics and skills that interviewers at Accenture expect are Data Science, Python, Artificial Intelligence, Machine Learning and SQL.
What are the top questions asked in Accenture Data Science Consultant interview?

Some of the top questions asked at the Accenture Data Science Consultant interview -

  1. Guest Estimates on how many sofa sold in a day in your c...read more
  2. How do you approach an end to end ML prob...read more
  3. Difference between boosting and bagging technique...read more

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Accenture Data Science Consultant Interview Process

based on 8 interviews

1 Interview rounds

  • Technical Round
View more

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Accenture Data Science Consultant Salary
based on 707 salaries
₹15 L/yr - ₹36 L/yr
28% more than the average Data Science Consultant Salary in India
View more details

Accenture Data Science Consultant Reviews and Ratings

based on 49 reviews

3.1/5

Rating in categories

3.0

Skill development

3.1

Work-life balance

2.9

Salary

3.6

Job security

2.8

Company culture

2.5

Promotions

2.8

Work satisfaction

Explore 49 Reviews and Ratings
S&C Global Network - AI - Hi Tech - Data Science Consultant

Gurgaon / Gurugram

4-8 Yrs

Not Disclosed

S&C Global Network - AI - CMT - Consultant Data Science

Gurgaon / Gurugram

5-10 Yrs

₹ 20-30.3456 LPA

S&C Global Network - AI - CMT - Consultant Data Science

Mumbai,

Hyderabad / Secunderabad

+1

5-10 Yrs

₹ 18.5-30.3456 LPA

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