Accenture is a leading global professional services company, renowned for its deep industry expertise, broad capabilities in digital, technology, and consulting, and commitment to delivering innovative solutions to clients worldwide. It is well-regarded for fostering a culture of continuous learning and development, making Accenture an ideal destination for top talent.

As a Data Scientist at Accenture in India, you will be at the forefront of transformative projects, leveraging advanced analytics, machine learning, and artificial intelligence to solve complex business challenges across various industries. This role demands strong analytical abilities, proficiency in programming languages like Python or R, and a thorough understanding of statistical modeling and data visualization tools.

To help you navigate the competitive Accenture interview process, Interview Query provides a comprehensive guide. Our guide covers the entire interview process, from commonly asked questions to practical tips, ensuring you're well-prepared to excel in your Data Scientist interview at Accenture.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Accenture as a data scientist. Whether you were contacted by an Accenture recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Accenture Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Accenture data scientist hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Accenture data scientist role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Accenture’s data systems, ETL pipelines, and SQL queries.

In the case of data scientist roles, take-home assignments regarding product metrics, analytics, and data visualization are incorporated. Apart from these, your proficiency against hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Accenture office. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the data scientist role at Accenture.

Quick Tips For Accenture Data Scientist Interviews

• Understand Accenture’s Business: Accenture serves a wide variety of industries, so familiarize yourself with the company's range of services and clients. Understanding how data science can be applied across different domains will help you in the case study questions.
• Be Proficient with Data Science Tools and Concepts: Accenture places a strong emphasis on technical skills. Make sure to brush up on machine learning algorithms, SQL, Python, and big data technologies.
• Demonstrate Problem-Solving Skills: Accenture values candidates who can think on their feet and solve problems efficiently. Practice responding to situational and case study questions that showcase your analytical and problem-solving abilities.

Typically, interviews at Accenture In India vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Write a function to find the maximum number in a list of integers. Given a list of integers, write a function that returns the maximum number in the list. If the list is empty, return None.

• Create a function convert_to_bst to convert a sorted list into a balanced binary tree. Given a sorted list, create a function convert_to_bst that converts the list into a balanced binary tree. The output binary tree should have a height difference of at most one between the left and right subtrees of all nodes.

• Write a function to simulate drawing balls from a jar. Write a function to simulate drawing balls from a jar. The colors of the balls are stored in a list named jar, with corresponding counts of the balls stored in a list called n_balls.

• Develop a function can_shift to check if one string can be shifted to become another. Given two strings A and B, write a function can_shift to return whether or not A can be shifted some number of places to get B.

• What are the drawbacks of having student test scores organized in the given layouts? Assume you have data on student test scores in two different layouts. Identify the drawbacks of these layouts and suggest formatting changes to make the data more useful for analysis. Additionally, describe common problems seen in "messy" datasets.

• How would you locate a mouse in a 4x4 grid using the fewest scans? You have a 4x4 grid with a mouse trapped in one of the cells. You can scan subsets of cells to know if the mouse is within that subset. Describe a strategy to locate the mouse using the fewest number of scans.

• How would you select Dashers for Doordash deliveries in NYC and Charlotte? Doordash is launching delivery services in New York City and Charlotte and needs a process for selecting dashers. Describe how you would decide which Dashers do these deliveries and whether the criteria for selection would be the same for both cities.

• What factors could bias Jetco's study on boarding times? Jetco, a new airline, had a study showing it has the fastest average boarding times. Identify factors that could have biased this result and what you would look into to verify the study's accuracy.

• How would you design an A/B test to evaluate a pricing increase for a B2B SAAS company? You work at a B2B SAAS company interested in testing different subscription pricing levels. Your project manager asks you to run a two-week-long A/B test to test an increase in pricing. Describe how you would design this test and determine if the pricing increase is a good business decision.

• How much should a ride-sharing app budget for a $5 coupon initiative? A ride-sharing app has a probability (p) of dispensing a $5 coupon to a rider and services (N) riders. Calculate the total budget needed for the coupon initiative.

• What is the probability of riders getting a coupon? A driver using the app picks up two passengers. Determine:

• The probability of both riders getting the coupon.

• The probability that only one of them will get the coupon.

• What is a confidence interval for a statistic and why is it useful? Explain what a confidence interval is, why it is important to know, and how to calculate it.

• What is the probability of finding an item on Amazon's website? Amazon has a warehouse system where items are located at different distribution centers. Given the probabilities that item X is available at warehouse A (0.6) or warehouse B (0.8), calculate the probability that item X would be found on Amazon's website.

• Is a coin fair if it comes up tails 8 times out of 10 flips? You flip a coin 10 times, resulting in 8 tails and 2 heads. Determine if the coin is fair.

• What are time series models and why are they needed? Describe what time series models are and explain why they are necessary when simpler regression models exist.

• How would you justify the complexity of building a neural network model and explain predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of the model and explain its predictions to non-technical stakeholders?

• How would you evaluate the suitability and performance of a decision tree model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will repay a personal loan. How would you evaluate whether a decision tree is the correct model, and how would you assess its performance before and after deployment?

• How does random forest generate the forest, and why use it over logistic regression? Explain how random forest generates its forest of trees. Additionally, discuss why one might choose random forest over other algorithms like logistic regression.

• How would you explain linear regression to a child, a first-year college student, and a seasoned mathematician? Explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician. Tailor your explanations to each audience's understanding level.

• What are the key differences between classification models and regression models? Describe the main differences between classification models and regression models.

What is the average salary for a Data Scientist at Accenture In India?

Q: What kind of projects do Data Scientists at Accenture in India typically work on?

Data Scientists at Accenture in India work on a variety of projects, ranging from predictive analytics and machine learning models to big data solutions and data visualization. They collaborate with global clients to solve complex business problems and deliver data-driven insights.

Q: What skills are essential for a Data Scientist role at Accenture?

To be successful as a Data Scientist at Accenture, proficiency in programming languages like Python or R, experience with machine learning algorithms, and strong statistical analysis skills are essential. Additionally, familiarity with big data technologies such as Hadoop and Spark, as well as skills in data visualization tools like Tableau or Power BI, are highly beneficial.

Q: What can I expect from the interview process for a Data Scientist position at Accenture in India?

The interview process typically includes an initial phone screening, followed by technical interviews that assess your coding, analytical, and problem-solving skills. You may also be given a case study or a real-world problem to solve. The final rounds often include interviews with senior leaders to evaluate your fit within the team and the company culture.

Q: What is the company culture like at Accenture in India?

Accenture in India promotes a collaborative and inclusive culture. They value continuous learning and innovation and encourage employees to take ownership of their professional development. The work environment is dynamic and offers opportunities to work with cutting-edge technologies and global clients.

Q: How can I prepare for an interview at Accenture for a Data Scientist position?

To prepare for an interview at Accenture, research the company and its recent projects. Practice common data science interview questions on Interview Query, review your technical skills, and be prepared to discuss your past projects and experiences. Make sure to highlight how your skills and experiences align with the role you are applying for.

Exploring a career as a Data Scientist at Accenture in India offers exciting opportunities and the potential to work on diverse, cutting-edge projects. If you want more insights about the company, check out our main Accenture India Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Accenture’s interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Accenture machine learning engineer interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!