Globallogic Data Scientist Interview Questions + Guide in 2025

Globallogic is a leader in digital engineering, helping brands design and build innovative digital products and experiences across various industries.

As a Data Scientist at Globallogic, you will be responsible for leveraging machine learning algorithms to extract insights from large datasets, enhance device performance, and improve user experience in the wireless and telecommunications domains. Key responsibilities include performing data extraction, transformation, and loading (ETL), conducting statistical analyses, and developing predictive models using Python, R, and other data science tools. The ideal candidate will possess a strong background in wireless technologies, have published research in relevant areas, and demonstrate proficiency in data visualization and analytics tools.

To excel in this role, you should have at least 5 years of experience in machine learning and data analytics, preferably within the wireless industry. A Master’s or PhD in a related field such as Computer Science, Electrical Engineering, or Statistics is preferred. Strong analytical capabilities, creative problem-solving skills, and the ability to communicate complex concepts clearly will also enhance your effectiveness in this position.

This guide will help you prepare for your interview by providing insights into the key areas of focus and expectations for the Data Scientist role at Globallogic, ensuring you are well-equipped to demonstrate your expertise and alignment with the company's goals.

The interview process for a Data Scientist role at GlobalLogic is structured and thorough, designed to assess both technical and interpersonal skills. Here’s a breakdown of the typical steps involved:

1. Application and Resume Screening

The process begins with candidates submitting their resumes and application materials. The recruitment team reviews these submissions to shortlist candidates based on their relevant experience, skills, and educational background. This initial screening is crucial as it sets the stage for the subsequent interview rounds.

2. Initial Screening Call

Following the resume screening, candidates typically engage in a 15-30 minute phone or video call with a recruiter or HR representative. This call serves as an introduction to the company and the role, allowing the recruiter to gauge the candidate's background, motivation for applying, and overall fit for the company culture. Expect questions about your previous work experience and your understanding of the Data Scientist role.

3. Technical Assessment

Candidates who pass the initial screening will undergo a technical assessment, which may include an online coding test or a take-home assignment. This assessment focuses on evaluating your proficiency in programming languages such as Python, R, or Java, as well as your understanding of machine learning algorithms, data manipulation, and statistical analysis. Be prepared to solve problems related to data extraction, transformation, and loading (ETL), as well as coding challenges that test your algorithmic thinking.

4. Technical Interview

Successful candidates from the technical assessment will be invited to a technical interview, typically conducted by a senior data scientist or a technical lead. This interview delves deeper into your technical skills, including your experience with machine learning frameworks, data visualization tools, and database management systems. Expect to discuss your past projects in detail, including the methodologies you employed and the outcomes achieved. You may also face scenario-based questions that assess your problem-solving abilities in real-world contexts.

5. Managerial Interview

After the technical interview, candidates may participate in a managerial round. This interview focuses on assessing your soft skills, such as communication, teamwork, and leadership abilities. Interviewers may ask about your experience working in teams, how you handle conflicts, and your approach to project management. This round is essential for determining how well you would fit into the team dynamics at GlobalLogic.

6. HR Interview

The final step in the interview process is typically an HR interview, where you will discuss compensation, benefits, and company culture. This round is also an opportunity for you to ask any questions you may have about the role or the company. The HR representative will assess your alignment with the company’s values and your long-term career aspirations.

7. Offer and Negotiation

If you successfully navigate all the interview stages, you may receive a job offer. This offer will detail the compensation package, benefits, and other relevant information. Candidates are encouraged to negotiate terms if necessary.

As you prepare for your interview, it’s essential to familiarize yourself with the types of questions that may be asked throughout the process.

Here are some tips to help you excel in your interview.

Understand the Technical Landscape

As a Data Scientist at GlobalLogic, you will be expected to have a strong grasp of machine learning algorithms, data extraction, and statistical analysis. Familiarize yourself with the specific technologies mentioned in the job description, such as Python, R, Spark, and SQL. Be prepared to discuss your experience with these tools and how you have applied them in real-world scenarios. Additionally, brush up on your knowledge of wireless analytics, as this is a key area for the role.

Prepare for Behavioral Questions

GlobalLogic values collaboration and a positive work environment. Expect behavioral questions that assess your ability to work in teams, handle conflicts, and adapt to changing situations. Use the STAR (Situation, Task, Action, Result) method to structure your responses, focusing on specific examples from your past experiences that demonstrate your problem-solving skills and teamwork.

Showcase Your Projects

Be ready to discuss your previous projects in detail, especially those related to machine learning and data analytics. Highlight your role, the challenges you faced, and the impact of your work. This not only demonstrates your technical skills but also your ability to contribute to the company's goals. If you have published papers or contributed to significant projects in the wireless industry, make sure to mention these as they align with the company's focus.

Communicate Clearly and Confidently

Effective communication is crucial in a collaborative environment. Practice explaining complex technical concepts in simple terms, as you may need to communicate your findings to non-technical stakeholders. During the interview, maintain a confident yet personable demeanor, showing enthusiasm for the role and the company.

Be Ready for Technical Assessments

Expect a mix of technical assessments, including coding challenges and problem-solving scenarios. Review common data structures and algorithms, as well as SQL queries and data manipulation techniques. Practice coding problems on platforms like LeetCode or HackerRank to sharpen your skills. Additionally, be prepared to discuss your thought process during these assessments, as interviewers often value how you approach problems as much as the final solution.

Embrace the Company Culture

GlobalLogic emphasizes a collaborative and flexible work environment. Research the company's values and culture, and think about how your personal values align with theirs. Be prepared to discuss why you want to work at GlobalLogic and how you can contribute to their mission of digital transformation.

Follow Up Thoughtfully

After the interview, send a thank-you email to express your appreciation for the opportunity to interview. Use this as a chance to reiterate your interest in the role and briefly mention any key points from the interview that you found particularly engaging. This not only shows professionalism but also keeps you on the interviewer's radar.

By following these tips and preparing thoroughly, you can position yourself as a strong candidate for the Data Scientist role at GlobalLogic. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at GlobalLogic. The interview process will likely assess your technical skills, problem-solving abilities, and understanding of machine learning and data analytics concepts. Be prepared to discuss your previous projects and experiences in detail, as well as demonstrate your knowledge of relevant tools and methodologies.

Machine Learning

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial. Be clear about the definitions and provide examples of each type.

How to Answer

Discuss the key characteristics of both supervised and unsupervised learning, including how they are used in practice.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, where the model tries to find patterns or groupings, like clustering customers based on purchasing behavior.”

2. Describe a machine learning project you have worked on. What challenges did you face?

This question assesses your practical experience and problem-solving skills.

How to Answer

Highlight a specific project, the challenges encountered, and how you overcame them.

Example

“I worked on a project to predict customer churn for a telecom company. One challenge was dealing with imbalanced data. I implemented techniques like SMOTE to balance the dataset and improve model performance, which ultimately led to a 15% increase in prediction accuracy.”

3. How do you handle overfitting in a machine learning model?

This question tests your understanding of model evaluation and optimization.

How to Answer

Discuss various techniques to prevent overfitting, such as regularization, cross-validation, and pruning.

Example

“To handle overfitting, I often use techniques like L1 and L2 regularization to penalize complex models. Additionally, I employ cross-validation to ensure that the model generalizes well to unseen data, and I might simplify the model by reducing the number of features.”

4. What is the purpose of feature engineering, and can you provide an example?

Feature engineering is a critical step in the data preparation process.

How to Answer

Explain the concept of feature engineering and its importance in improving model performance.

Example

“Feature engineering involves creating new features from existing data to improve model accuracy. For instance, in a sales prediction model, I created a feature for ‘seasonality’ by extracting the month from the date, which helped the model better capture seasonal trends in sales data.”

Statistics & Probability

1. Explain the Central Limit Theorem and its significance.

This question evaluates your understanding of statistical concepts.

How to Answer

Define the Central Limit Theorem and discuss its implications in data analysis.

Example

“The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the original distribution. This is significant because it allows us to make inferences about population parameters even when the population distribution is unknown.”

2. How do you assess the statistical significance of your results?

Understanding statistical significance is essential for validating your findings.

How to Answer

Discuss methods such as p-values, confidence intervals, and hypothesis testing.

Example

“I assess statistical significance by calculating p-values and comparing them to a significance level, typically 0.05. If the p-value is less than this threshold, I reject the null hypothesis, indicating that the results are statistically significant.”

3. What is the difference between Type I and Type II errors?

This question tests your knowledge of hypothesis testing.

How to Answer

Clearly define both types of errors and their implications.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. Understanding these errors is crucial for interpreting the results of hypothesis tests accurately.”

4. Can you explain what a p-value is?

This question assesses your grasp of statistical testing.

How to Answer

Define p-value and its role in hypothesis testing.

Example

“A p-value measures the probability of obtaining results at least as extreme as the observed results, assuming the null hypothesis is true. A low p-value indicates strong evidence against the null hypothesis, suggesting that the observed effect is statistically significant.”

Data Manipulation & Tools

1. What experience do you have with SQL? Can you write a query to join two tables?

SQL skills are essential for data manipulation.

How to Answer

Discuss your experience with SQL and provide a brief example of a query.

Example

“I have extensive experience with SQL for data extraction and manipulation. For instance, to join two tables, I would use a query like: ‘SELECT * FROM table1 INNER JOIN table2 ON table1.id = table2.id;’ This allows me to combine data from both tables based on a common key.”

2. How do you handle missing data in a dataset?

This question evaluates your data cleaning skills.

How to Answer

Discuss various strategies for dealing with missing data.

Example

“I handle missing data by first assessing the extent of the missingness. Depending on the situation, I might use imputation techniques, such as filling in missing values with the mean or median, or I may choose to remove rows or columns with excessive missing data.”

3. Describe your experience with data visualization tools. Which do you prefer and why?

Data visualization is key for presenting findings.

How to Answer

Discuss your experience with specific tools and your preferences.

Example

“I have experience with tools like Tableau and Matplotlib. I prefer Tableau for its user-friendly interface and ability to create interactive dashboards, which are great for presenting insights to stakeholders. However, I use Matplotlib for more customized visualizations in Python.”

4. Can you explain the concept of ETL and its importance?

Understanding ETL processes is crucial for data handling.

How to Answer

Define ETL and discuss its role in data management.

Example

“ETL stands for Extract, Transform, Load. It’s a process used to gather data from various sources, transform it into a suitable format, and load it into a data warehouse. ETL is important because it ensures that data is clean, consistent, and ready for analysis, which is essential for making informed business decisions.”