Accenture is a global professional services company with leading capabilities in digital, cloud, and security. In India, Accenture has established itself as a forefront player, leveraging advanced technology to drive meaningful business insights and innovation.

The Machine Learning Engineer position at Accenture is designed for individuals with a strong foundation in machine learning algorithms, data processing, and model deployment. The role demands proficiency in Python, R, TensorFlow, PyTorch, and also a deep understanding of machine learning frameworks and libraries. As a Machine Learning Engineer, you'll collaborate with cross-functional teams to create predictive models, optimize algorithms, and contribute to transformative AI solutions for diverse industry challenges.

If you're aiming to join this prestigious company, Interview Query is here to help. This guide provides insights into the interview process, commonly asked questions, and valuable tips to ace your Accenture Machine Learning Engineer interview. Let's dive in!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Accenture as a Machine Learning Engineer. 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 Machine Learning Engineer 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 Machine Learning Engineer role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around machine learning systems, algorithms, and coding problems.

In the case of machine learning roles, take-home assignments regarding model development, data preprocessing, and optimization tasks are incorporated. Apart from these, your proficiency in machine learning fundamentals, statistical analysis, and familiarity with frameworks such as TensorFlow and PyTorch 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 Machine Learning Engineer role at Accenture.

Quick Tips For Accenture Machine Learning Engineer Interviews

• Should contain three tips for interviewing for this specific company based on interview experiences.

Example:

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Accenture interview include:

• Understand Accenture's Service Lines: Accenture focuses on a wide range of services, from consulting to technology. Study Accenture’s different service lines and understand how machine learning solutions can be implemented into these areas.
• Emphasize Consulting Skills: As a consultancy firm, Accenture places a high value on problem-solving and communication skills. Be prepared to discuss how you have used machine learning to solve real-world problems and articulate your thought process clearly.
• Technical Proficiency: Accenture interviews assess how well you can apply machine learning algorithms to business problems. Brush up on your knowledge of algorithms, data structures, and machine learning frameworks like TensorFlow and PyTorch.

Typically, interviews at Accenture In India vary by role and team, but commonly Machine Learning Engineer 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 the same index 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? Analyze the provided datasets to identify drawbacks in their organization. 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? Given a 4x4 grid with a mouse in one cell, determine the cell by scanning subsets of cells. Each scan reveals if the mouse is in the subset but not the exact location. Devise a strategy to find the mouse with the minimum number of scans.

• How would you select Dashers for Doordash deliveries in NYC and Charlotte? Doordash is launching in NYC and Charlotte and needs a process for selecting delivery drivers. Determine the criteria for selecting Dashers and whether the criteria should differ between the two cities.

• What factors could bias Jetco's study on boarding times? Jetco's study shows it has the fastest average boarding times among airlines. Identify potential biases in the study and what factors you would investigate to validate the results.

• How would you design an A/B test to evaluate a pricing increase for a B2B SAAS company? A B2B SAAS company wants to test different subscription pricing levels. Design a two-week A/B test to evaluate the impact of a pricing increase. Determine how to assess if the pricing change is a good business decision.

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

• What is the probability of both riders getting the coupon? A driver using the app picks up two passengers. Determine the probability that both riders will receive the coupon.

• What is the probability that only one of them will get the coupon? A driver using the app picks up two passengers. Determine the probability that only one of the riders will receive the coupon.

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

• What is the probability that item X would be found 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) and warehouse B (0.8), calculate the probability that item X would be found on Amazon's website.

• Is this a fair coin? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair.

• What are time series models and why do we need them? Describe what time series models are and explain why they are necessary compared to less complicated regression models.

• 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? How would you evaluate 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. Additionally, why would you 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.

Exploring opportunities as a Machine Learning Engineer at Accenture in India can be a game-changer for your career. With a blend of innovative projects and a dynamic working environment, Accenture offers a platform to grow and excel. If you want more insights about the company, check out our main Accenture India Interview Guide, where we have covered numerous interview questions that could come up. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Accenture India’s interview process for different positions.

At Interview Query, we empower you with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to master every Accenture India Machine Learning Engineer interview question and challenge.

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

Good luck with your interview!