Ericsson, a global leader in telecommunications and technology innovation, is dedicated to pushing the boundaries of what’s possible. Renowned for its pioneering role in network technology, Ericsson continually seeks to innovate within the realms of 5G, IoT, and artificial intelligence. As a Data Scientist at Ericsson, you will be working on transformative projects involving large-scale AI and machine learning solutions, collaborating with diverse datasets from telecom networks to create predictive models, recommendation engines, and anomaly detection systems. This role demands strong coding skills in Python, expertise in machine learning frameworks, and experience in statistical modeling. Join us as we craft groundbreaking solutions to some of the world’s most complex challenges. This guide aims to help you navigate the interview process at Ericsson, focusing on the steps involved and the types of questions you may encounter. Let's get started!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Ericsson as a Data Scientist. Whether you were contacted by an Ericsson 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 Ericsson 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 Ericsson 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 Ericsson Data Scientist role usually is conducted through virtual means, including video conference and screen sharing.

Questions in this stage may revolve around ML and DL-oriented questions, basic Python coding, and practical data analysis using tools like Pandas. You might need to discuss your previous projects and answer questions on model evaluation metrics, logistic regression, and activation functions.

Presentation or small coding tasks, like explaining the difference between ML and DL, the importance of activation functions (like ReLU), and basic statistical concepts (mean, median), can be expected.

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 Ericsson office or over several days via video conferences.

Detailed deep dives into machine learning algorithms, coding using Python or similar languages, demonstrating your understanding of core data science concepts (e.g., A/B testing, classification metrics, recommendation systems), and solving real-world cases are likely to happen during these rounds.

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

Quick Tips For Ericsson Data Scientist Interviews

• Brush Up on Your Fundamentals: Ericsson interviews often cover a wide array of data science topics. Ensure you have strong foundations in ML algorithms, statistical methods, and Python programming.
• Be Prepared for Multiple Rounds: You may face several rounds of technical interviews and coding challenges, including coding on platforms like HackerRank. Practice thoroughly to manage time effectively and cover as many areas as possible.
• Express Patience with the Process: Understand that the hiring process could be lengthy, including waiting periods between rounds and feedback. Maintain a professional attitude and stay patient throughout.

Typically, interviews at Ericsson 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 be balanced, meaning the height difference between the left and right subtree of all the nodes should be at most one.

• 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 determine 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.

• How would you explain linear regression to a child, a college student, and a 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.

• How would you evaluate the suitability and performance of a decision tree model for predicting loan repayment? As a data scientist at a bank, you need to build a decision tree model to predict if a borrower will repay a personal loan. Evaluate whether a decision tree is the correct model and how you would assess its performance before and after deployment.

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

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

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

• How much should we budget for a $5 coupon initiative in a ride-sharing app? 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 riders getting a coupon in a ride-sharing app? 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 useful to know the confidence interval for a statistic, and how to calculate it.

• What is the probability of finding an item on Amazon's website given warehouse availability? Amazon has a warehouse system where items are located at different distribution centers. In one city, the probability that item X is available at warehouse A is 0.6 and at warehouse B is 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, and it comes up tails 8 times and heads twice. Determine if this coin is fair.

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

• What are the drawbacks of the given data organization, and how would you reformat it for better analysis? 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 find 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, and what would you investigate? Jetco, a new airline, had a study showing it has the fastest average boarding times. Identify potential biases in this result and describe what you would investigate to validate the study.

• 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. Describe how you would design a two-week-long A/B test to evaluate a pricing increase and determine if it is a good business decision.

What is the average salary for a Data Scientist at Ericsson?

Q: What is the typical interview process for a Data Scientist position at Ericsson?

The interview process generally includes an initial HR screening, followed by technical and managerial interviews. Typically, you can expect a mix of technical rounds focusing on machine learning, data science concepts, and coding challenges. You may also have a managerial interview to discuss your career goals and past experiences.

Q: What topics should I prepare for the technical interviews at Ericsson?

You should prepare for questions on machine learning (ML) and deep learning (DL) concepts, programming in Python, data analysis using pandas, and algorithms. Expect questions on topics like logistic regression, activation functions, A/B testing, classification metrics, recommendation systems, and fundamental data science concepts.

Q: What skills are essential for the Data Scientist role at Ericsson?

Key skills include strong programming abilities in Python, experience with machine learning and statistical models, SQL proficiency, and familiarity with tools like Sklearn, TensorFlow, or PyTorch. Effective communication skills are also important, as you may need to present technical findings to non-technical stakeholders.

Q: How can I prepare for an interview at Ericsson?

To prepare for an interview at Ericsson, thoroughly review the job description and brush up on relevant technical skills. Practice common interview questions using Interview Query, and be prepared to discuss your past projects and how they relate to the job requirements. Also, practice coding problems and data analysis tasks to demonstrate your programming proficiency.

Q: What can I expect in terms of feedback and follow-up during the interview process?

Feedback and follow-up can vary. In some cases, candidates have reported delays or a lack of communication post-interview. It's advisable to stay proactive and follow up with the HR team to get updates on your application status.

Navigating the interview process for a Data Scientist position at Ericsson can be quite a mixed experience. Although the interviews range from technical deep dives to managerial discussions, the overall candidate feedback reflects a pattern of inefficiencies and lack of professionalism. A significant number of candidates have experienced abrupt communication cuts post-interview, often after receiving verbal offers or undergoing extensive rounds of evaluation. This not only discourages potential talent but also reflects poorly on Ericsson's recruitment practices.

Yet, for those who manage to navigate through these hurdles, the role itself presents a stimulating challenge with immense potential for growth. As Ericsson pushes the boundaries of AI and machine learning innovations, you can expect to work on cutting-edge projects that tackle some of today’s most pressing technological challenges. This aspect could be incredibly appealing for aspirants passionate about making a tangible impact with their data science skills.

For more insights on Ericsson’s interview process, check out our comprehensive Ericsson Interview Guide. We've detailed numerous interview questions and tips that can help you prepare better. Remember, successful interviewing is as much about preparation as it is about showcasing your expertise.

At Interview Query, we equip you with the right tools, knowledge, and strategies to boost your confidence and excel in every interview stage. If you have any questions or need further guidance, don't hesitate to reach out.

Good luck with your interview!