Accenture Data Scientist Interview Questions & Experience Guide
Company Name: Accenture
Position: Data Scientist
Application Process: Applied via the company website and was interviewed in August 2021.
Interview Rounds:
- Round 1 - Technical Interview:
- Questions Asked:
- How do you choose which ML model to use?
- Your Approach: I discussed the importance of understanding the problem, data characteristics, and model assumptions. I also mentioned cross-validation and performance metrics as key factors in model selection.
- Outcome: The interview was rescheduled as the interviewer didn’t join the call on the scheduled date. The interviewer was unprofessional and rude during the rescheduled session.
- Questions Asked:
Interview Preparation Tips:
- Be prepared for rescheduling or delays, as such issues can occur.
- Stay calm and professional, even if the interviewer is not.
- Brush up on fundamental ML concepts and be ready to explain your thought process clearly.
Conclusion:
The experience was disappointing due to the interviewer’s unprofessional behavior. However, it reinforced the importance of patience and adaptability during the interview process. Future candidates should focus on their preparation and not let external factors affect their confidence.
Company Name: Accenture
Position: Data Scientist
Location: National Institute of Technology, Surathkal
Application Process: Applied via campus placement before May 2023.
Interview Rounds:
-
Round 1 - Aptitude Test:
- Questions Asked: Coding MCQs, quantitative aptitude, and logical reasoning.
- Your Approach: Focused on solving the coding and aptitude questions systematically, ensuring accuracy and time management.
- Outcome: Cleared the round successfully.
-
Round 2 - Technical Interview:
- Questions Asked: Questions on Python, SQL, ML statistics, and data analytics. Detailed project explanation and Q/A over the project.
- Your Approach: Prepared thoroughly on Python, SQL, and ML concepts. Explained my project clearly, highlighting key insights and methodologies.
- Outcome: Performed well and advanced to the next stage.
Preparation Tips:
- Focus on core Python and SQL concepts.
- Revise ML statistics and data analytics fundamentals.
- Practice explaining your projects concisely and clearly.
Conclusion:
The interview process was smooth and well-structured. Preparing thoroughly for technical concepts and being clear about my project details helped me perform well. For future candidates, I’d recommend practicing coding and aptitude questions regularly and being confident in explaining your projects.
Company Name: Accenture
Position: Data Scientist
Application Process: The application process involved three rounds: Resume Shortlist, Coding Test, and Technical Interview.
Interview Rounds:
-
Round 1 - Resume Shortlist:
- Details: The first round was a resume screening process. Ensure your resume is concise and avoids unnecessary details like photos, gender, age, or address.
- Outcome: Successfully shortlisted for the next round.
-
Round 2 - Coding Test:
- Questions Asked: The test consisted of a single coding question.
- Your Approach: Focused on solving the problem efficiently, ensuring the code was optimized and error-free.
- Outcome: Cleared the coding round and moved to the technical interview.
-
Round 3 - Technical Interview:
- Questions Asked: The questions were not yet disclosed. Will update once the results are declared.
- Your Approach: Prepared thoroughly by revising Data Structures, Algorithms, and Operating Systems concepts.
- Outcome: Awaiting results.
Preparation Tips:
- Practice Data Structures and Algorithms (DSA) regularly.
- Focus on networking and OS-based multiple-choice questions.
- Revise topics like recursion, logic gates, and core data structures.
Conclusion:
The interview process was structured and challenging. Preparing well for DSA and technical concepts is crucial. I recommend future candidates to focus on problem-solving and clarity in their resumes.
Company Name: Accenture
Position: Data Scientist
Location: VYWS’s Prof Ram Meghe Institute of Technology & Research Badnera, Amravati
Application Process: Applied via campus placement in June 2022.
Interview Rounds:
-
Round 1 - Resume Shortlist:
-
Questions Asked: N/A (Resume screening round)
-
Your Approach: Ensured the resume was crisp and highlighted relevant skills like Advanced Excel, Tableau, SQL, and basic Python.
-
Outcome: Successfully shortlisted for the next round.
-
Round 2 - Aptitude Test:
-
Questions Asked: Simple aptitude questions.
-
Your Approach: Focused on accuracy and speed while solving the questions.
-
Outcome: Cleared the round and proceeded further in the process.
Preparation Tips:
- Topics to prepare: Advanced Excel, Tableau, SQL, and basic Python.
- Stay updated and responsive to communication (phone or email) within the given timeframe.
Conclusion:
Overall, the interview process was smooth. Keeping the resume concise and being well-prepared for aptitude tests helped. Future candidates should focus on mastering the basics and ensuring timely responses to communications.
Company Name: Accenture
Position: Data Scientist
Application Process: Applied through campus placement.
Interview Rounds:
-
Round 1 - Aptitude Test:
- Questions Asked: The test had three sections: English, Logical Reasoning, and Technical MCQ. The difficulty level was easy to medium.
- Your Approach: Focused on time management and accuracy, especially in the Logical Reasoning and Technical MCQ sections.
- Outcome: Cleared the round successfully.
-
Round 2 - Case Study:
- Questions Asked: A Data Science problem was given, focusing on statistics, probability, and machine learning concepts.
- Your Approach: Tried to explain the solution step-by-step, emphasizing my understanding of the underlying concepts.
- Outcome: Performed well and advanced to the next stage.
Preparation Tips:
- The interview is highly conceptual. A strong grasp of statistics, probability, and machine learning is essential.
- Practice solving case studies to improve problem-solving skills and clarity of thought.
Conclusion:
The overall experience was smooth, and the interviewers were supportive. I would advise future candidates to focus on core concepts and practice explaining their thought process clearly during case studies.
Company Name: Accenture
Position: Data Scientist
Location: [Not specified]
Application Process: Applied via Campus Placement before September 2020.
Interview Rounds:
-
Round 1 - HR Interview:
- Questions Asked: Nothing much technical.
- Your Approach: Focused on presenting myself professionally and ensuring fluency in English.
- Outcome: Cleared the round successfully.
-
Round 2 - Aptitude Test:
- Questions Asked: General aptitude questions.
- Your Approach: Prepared by practicing common aptitude problems and improving time management.
- Outcome: Cleared the round.
-
Round 3 - Technical Interview:
- Questions Asked: Questions related to data science, SQL, and business process understanding.
- Your Approach: Revised key concepts in data science and practiced SQL queries beforehand.
- Outcome: Successfully cleared the round.
Preparation Tips:
- Dress in formals for the interview.
- Ensure fluency in English, as it may be important depending on the interview panel.
- Be clear and confident about what you are discussing.
Conclusion:
Overall, the interview process was smooth, and the key to success was being well-prepared and professional. Practicing aptitude questions and brushing up on technical skills helped a lot. For future candidates, focus on clarity and confidence in your responses.
Company Name: Accenture
Position: Data Scientist
Application Process: I applied through a recruitment consultant and was interviewed before April 2023.
Interview Rounds:
-
Round 1 - Technical Round:
- Questions Asked:
- What different splitting criteria are applied in decision trees? Why does a random forest work better?
- Your Approach: I explained the common splitting criteria like Gini impurity and information gain, and then discussed how random forests reduce overfitting by combining multiple decision trees.
- Outcome: I passed this round.
- Questions Asked:
-
Round 2 - Technical Round:
- Questions Asked:
- Describe the process to generate embeddings on your own dataset.
- Your Approach: I outlined steps like data preprocessing, choosing an embedding method (e.g., Word2Vec or TF-IDF), and training the model on the dataset.
- Outcome: I successfully cleared this round as well.
- Questions Asked:
Preparation Tips:
- Brush up on fundamental machine learning concepts, especially decision trees and ensemble methods.
- Understand embedding techniques and their practical applications.
- Practice explaining technical concepts clearly and concisely.
Conclusion:
The interview process was smooth, and the questions were focused on practical applications of data science. I felt well-prepared, but I could have spent more time on hands-on embedding implementations. My advice for future candidates is to focus on both theoretical knowledge and practical implementation.
Company Name: Accenture
Position: Data Scientist
Application Process: Approached by the company for the interview process, which took place before April 2023.
Interview Rounds:
-
Round 1 - Technical Round:
-
Questions Asked: The interviewer asked questions based on my projects and resume.
-
Your Approach: I focused on explaining my projects in detail, highlighting my contributions, methodologies, and the outcomes. I also ensured I could discuss any technical aspects mentioned in my resume.
-
Outcome: The round went smoothly, and I advanced to the next stage.
-
Round 2 - HR Round:
-
Questions Asked: The discussion revolved around salary expectations and other HR-related topics.
-
Your Approach: I was honest about my salary expectations and also took the opportunity to ask about the company culture and growth opportunities.
-
Outcome: The round was more of a formality, and the discussion was straightforward.
Preparation Tips:
- Focus on thoroughly understanding your resume and projects, as technical questions are likely to be based on them.
- Be prepared to discuss your contributions and the impact of your work.
- For the HR round, research typical salary ranges for the role and be clear about your expectations.
Conclusion:
The interview process with Accenture was smooth and well-structured. The technical round was project-focused, which allowed me to showcase my skills effectively. The HR round was more about aligning expectations. My advice for future candidates is to be confident in discussing your work and to prepare for salary negotiations in advance.
Company Name: Accenture
Position: Data Scientist
Application Process: Walk-in interview before April 2022.
Interview Rounds:
-
Round 1 - Resume Shortlist:
-
Questions Asked: N/A (Resume screening round)
-
Your Approach: Ensured my resume was error-free and highlighted relevant skills and projects.
-
Outcome: Passed to the next round.
-
Round 2 - Aptitude Test:
-
Questions Asked: Aptitude test covering quantitative topics and basic programming languages.
-
Your Approach: Prepared by revising quantitative aptitude and basic programming concepts.
-
Outcome: Cleared the test and moved to the technical round.
-
Round 3 - Technical Round:
-
Questions Asked:
- Technical questions related to my skill set.
- Detailed questions about my projects.
-
Your Approach: Focused on explaining my projects thoroughly and demonstrated my understanding of the technical skills mentioned in my resume.
-
Outcome: Successfully answered the questions and advanced further in the process.
Preparation Tips:
- Study the fundamentals of statistics and machine learning algorithms.
- Be well-versed with at least one of your projects from end to end, including its working and implementation details.
Conclusion:
The interview process was smooth, and the questions were aligned with my skill set. I made sure to present my projects clearly, which helped me perform well. For future candidates, I’d recommend focusing on core concepts and being confident about your project work.
Company Name: Accenture
Position: Data Scientist
Application Process: I applied through a recruitment consultant and was interviewed before June 2023.
Interview Rounds:
-
Round 1 - Technical Round:
- Questions Asked:
- Merge two dataframes.
- Question on joins.
- Your Approach: For the first question, I explained how to merge dataframes using functions like
pd.merge()in Python, discussing different types of joins (inner, outer, left, right). For the second question, I elaborated on the concept of joins in SQL and how they differ from dataframe merges. - Outcome: I passed this round.
- Questions Asked:
-
Round 2 - Technical Round:
- Questions Asked:
- Case study on utilities.
- Python coding question.
- Your Approach: For the case study, I analyzed the problem by breaking it down into smaller components and suggested data-driven solutions. For the Python coding question, I wrote efficient code and explained my thought process.
- Outcome: I cleared this round as well.
- Questions Asked:
Preparation Tips:
- Brush up on Python and SQL, especially data manipulation and joins.
- Practice case studies to improve problem-solving skills.
- Be ready to explain your approach clearly during the interview.
Conclusion:
The interview process was smooth, and the questions were aligned with the role’s requirements. I felt well-prepared, but practicing more case studies beforehand would have been beneficial. My advice to future candidates is to focus on both technical skills and clear communication.
Company Name: Accenture
Position: Data Scientist
Application Process: The application process began with a resume shortlist round, followed by a coding test and a technical interview.
Interview Rounds:
-
Round 1 - Resume Shortlist:
-
Questions Asked: N/A (Resume screening)
-
Your Approach: Ensured my resume was concise and highlighted relevant skills and projects.
-
Outcome: Successfully shortlisted for the next round.
-
Round 2 - Coding Test:
-
Questions Asked: Basic for-loop question.
-
Your Approach: Focused on writing clean and efficient code.
-
Outcome: Cleared the coding round.
-
Round 3 - Technical Interview:
-
Questions Asked: SQL questions and project-related questions.
-
Your Approach: Explained my projects in detail and demonstrated my SQL knowledge.
-
Outcome: Performed well and received positive feedback.
Preparation Tips:
- Be confident and ensure you have a strong grasp of basic concepts.
- Practice coding problems, especially loops and SQL queries.
- Be ready to discuss your projects in detail.
Conclusion:
Overall, the interview process was smooth. I felt well-prepared, but I could have practiced more SQL queries to feel even more confident. My advice to future candidates is to focus on the basics and be clear about your projects.
Company Name: Accenture
Position: Data Scientist
Location: [Location not specified]
Application Process: [Application process details not provided]
Interview Rounds:
-
Round 1 - Resume Shortlist:
-
Questions Asked: Proper alignment and formatting of the resume were emphasized.
-
Your Approach: Ensured the resume was well-structured and clearly highlighted relevant projects and skills.
-
Outcome: Successfully shortlisted for the next round.
-
Round 2 - One-on-one Interview:
-
Questions Asked: Questions related to the projects I had worked on, along with some light coding questions.
-
Your Approach: Discussed my projects in detail, explaining the methodologies and outcomes. For coding, I focused on clarity and efficiency.
-
Outcome: Advanced to the next round.
-
Round 3 - One-on-one Interview:
-
Questions Asked: Further in-depth questions about my projects.
-
Your Approach: Provided detailed explanations, including challenges faced and how I addressed them.
-
Outcome: [Outcome not specified]
Preparation Tips:
- Thoroughly review your previous projects, as they are a key focus during the interviews.
Conclusion:
The interview process was project-centric, with a strong emphasis on discussing past work. Ensuring clarity and depth in project explanations was crucial. Future candidates should be well-prepared to discuss their projects in detail.
Company Name: Accenture
Position: Data Scientist
Application Process: I applied through campus placement and was interviewed before July 2023.
Interview Rounds:
-
Round 1 - Aptitude Test:
- Questions Asked: MCQs based on Statistics and Machine Learning.
- Your Approach: I reviewed basic statistical concepts and ML algorithms to tackle the MCQs.
- Outcome: Cleared the round successfully.
-
Round 2 - Technical Round:
- Questions Asked: Questions based on skills, tools, and concepts relevant to the role.
- Your Approach: I focused on explaining my understanding of key tools and concepts clearly.
- Outcome: Advanced to the next round.
-
Round 3 - One-on-one Round:
- Questions Asked: Questions about my projects and internships.
- Your Approach: I discussed my projects in detail, highlighting my contributions and learnings.
- Outcome: Moved forward to the HR round.
-
Round 4 - HR Round:
- Questions Asked: General questions about teamwork in projects, etc.
- Your Approach: I shared examples of collaborative projects and how I contributed to team success.
- Outcome: Received positive feedback and cleared the round.
Preparation Tips:
- Brush up on statistical concepts and ML algorithms for the aptitude test.
- Be thorough with your projects and internships as they are a key focus in the technical and one-on-one rounds.
- Practice explaining your teamwork experiences clearly for the HR round.
Conclusion:
Overall, the interview process was smooth and well-structured. I felt prepared for each round, but I could have spent more time practicing problem-solving for the aptitude test. My advice to future candidates is to focus on both technical and soft skills, as Accenture values a balanced approach.
Company Name: Accenture
Position: Data Scientist
Application Process: The application was likely through campus placement or online recruitment, though the exact method wasn’t specified.
Interview Rounds:
-
Round 1 - Aptitude Round:
- Questions Asked: General aptitude questions covering quantitative ability, logical reasoning, and verbal skills.
- Your Approach: Focused on solving problems quickly and accurately, brushing up on basic aptitude topics beforehand.
- Outcome: Cleared this round successfully.
-
Round 2 - Skill Round:
- Questions Asked: Deep technical questions related to statistics, machine learning, and data science concepts.
- Your Approach: Prepared by revising core ML and stats topics, and practiced explaining projects and internships in detail.
- Outcome: Passed this round, but it was challenging due to the depth of questions.
-
Round 3 - HR Round:
- Questions Asked: Behavioral questions, discussions about prior internships/projects, and general fit for the role.
- Your Approach: Stayed honest and concise, highlighting relevant experiences and enthusiasm for the role.
- Outcome: Successfully cleared this round.
Preparation Tips:
- Focus on strengthening your fundamentals in statistics and machine learning.
- Be ready to discuss your projects or internships in detail, as they ask in-depth questions about your work.
- Practice aptitude and problem-solving skills for the initial rounds.
Conclusion:
The interview process was rigorous, especially the technical rounds, which required a solid understanding of ML and stats. Preparing thoroughly for both technical and behavioral aspects was key. For future candidates, I’d recommend dedicating ample time to revising core concepts and being ready to explain your work clearly.
Company Name: Accenture
Position: Data Science Practitioner
Application Process: I applied for the position and received an interview invitation, which was scheduled for 2 pm.
Interview Rounds:
- Round 1 - Technical Interview:
- Questions Asked: Basic to mid-range questions related to data science.
- Your Approach: I answered the questions to the best of my ability, but the interviewer seemed disengaged.
- Outcome: The interviewer suggested concluding the call early and asked about my expected salary.
Conclusion:
The interview experience was unusual due to the interviewer’s apparent fatigue. Despite this, I remained professional and answered the questions as clearly as possible. For future candidates, I’d advise staying composed and adaptable, even in unexpected situations.
Company Name: Accenture
Position: Data Scientist
Application Process: Applied through the company’s career portal. The process was well-structured, with clear communication throughout.
Interview Rounds:
-
Round 1 - Technical Interview:
- Questions Asked: Relevant and challenging questions related to technical skills and problem-solving abilities.
- Your Approach: Showcased my expertise by providing detailed answers and practical examples.
- Outcome: Successfully cleared the round.
-
Round 2 - HR Interview:
- Questions Asked: Discussed my background, career goals, and fit for the role and company culture.
- Your Approach: Highlighted my enthusiasm for the role and alignment with Accenture’s values.
- Outcome: Positive feedback and moved forward in the process.
Conclusion:
The overall experience was great, with professional interviewers and a smooth process. It was a fantastic opportunity to demonstrate my skills and learn more about the company. For future candidates, I’d recommend thoroughly preparing for both technical and behavioral questions to ensure a strong performance.
Company Name: Accenture
Position: Data Scientist
Application Process: I applied for the Data Scientist role through their online application portal. The process involved two technical rounds followed by a managerial and HR round.
Interview Rounds:
-
Round 1 - Technical Interview:
- Questions Asked: The interviewer focused heavily on MLOps-related questions, such as pipeline management, deployment strategies, and CI/CD processes. Surprisingly, they didn’t delve into any of my past projects.
- Your Approach: I tried to relate my experience with MLOps tools and frameworks, explaining how I would design a pipeline for a given scenario. I also discussed best practices for deployment and CI/CD in machine learning projects.
- Outcome: I passed this round, but the feedback was to brush up on more advanced MLOps concepts.
-
Round 2 - Technical Interview:
- Questions Asked: This round was similar to the first, with deeper questions about scaling ML models, monitoring pipelines, and handling model drift.
- Your Approach: I shared my understanding of scaling challenges and how I would address them using tools like Kubernetes or cloud-based solutions. I also talked about monitoring techniques and handling model drift in production.
- Outcome: I cleared this round as well, but the interviewer suggested I gain more hands-on experience with large-scale deployments.
-
Round 3 - Managerial and HR Interview:
- Questions Asked: This round was a mix of behavioral and situational questions, such as how I handle conflicts in a team, my long-term career goals, and why I wanted to join Accenture.
- Your Approach: I answered honestly, aligning my career goals with the company’s vision and sharing examples of how I’ve resolved conflicts in past projects.
- Outcome: The round went well, and I received positive feedback about my communication and alignment with the company’s values.
Preparation Tips:
- Focus on MLOps concepts, especially pipeline design, deployment, and CI/CD.
- Brush up on scaling and monitoring techniques for ML models.
- Be prepared to discuss behavioral and situational questions confidently.
Conclusion:
Overall, the interview process was challenging but insightful. While I cleared all rounds, I realized the importance of hands-on experience with MLOps tools. For future candidates, I’d recommend diving deep into practical aspects of MLOps and being ready to discuss real-world scenarios in detail.
Company Name: Accenture
Position: Data Scientist
Application Process: Applied through campus placement.
Interview Rounds:
-
Round 1 - Technical Interview:
-
Questions Asked: Basic questions on statistics, SQL, and machine learning.
-
Your Approach: Since it was my first company interview, I wasn’t fully prepared. I answered the questions to the best of my ability but felt I could have done better with more preparation.
-
Outcome: The interview was chill, but I didn’t advance further due to lack of preparation.
Preparation Tips:
- Brush up on fundamental statistics, SQL, and machine learning concepts.
- Practice explaining your thought process clearly during interviews.
Conclusion:
It was a learning experience for me. I realized the importance of thorough preparation, especially for technical roles. Future candidates should ensure they are well-versed in the basics and practice mock interviews to build confidence.
Company Name: Accenture
Position: Data Scientist
Application Process: The entire interview process, from the first round to receiving the offer letter, took around 3 weeks. The first round was conducted virtually, and the second round was also virtual but required visiting their nearby office location. During the second round, they provided a laptop for attending the virtual interview.
Interview Rounds:
-
Round 1 - Virtual Interview:
- Questions Asked: Details about the specific questions asked in this round were not provided.
- Your Approach: The candidate attended the virtual interview from their preferred location.
- Outcome: Successfully cleared this round and proceeded to the next stage.
-
Round 2 - Virtual Interview (On-site):
- Questions Asked: Details about the specific questions asked in this round were not provided.
- Your Approach: The candidate visited the nearby office location where they were provided with a laptop to attend the virtual interview.
- Outcome: Cleared this round and received the offer letter shortly after.
Conclusion:
The overall experience was smooth, with clear communication from the company throughout the process. The hybrid setup for the second round (virtual but on-site) was unique but manageable. Future candidates should be prepared for a mix of virtual and on-site components, even if the interviews are technically virtual.