GSK Data Scientist Interview Questions + Guide in 2025

GSK is a global biopharmaceutical company dedicated to uniting science, technology, and talent to advance healthcare and improve the lives of billions of people.

As a Data Scientist at GSK, you will be instrumental in analyzing diverse data sources, including structured and unstructured data, to build innovative machine learning algorithms that drive data-driven decision-making across various business functions. Your key responsibilities will include developing high-quality analytical solutions for Medical Affairs, collaborating with cross-functional teams, and actively engaging with stakeholders to uncover insights that influence medicine development. You will also have the opportunity to mentor and coach other data scientists, fostering a culture of learning and innovation within the organization.

To excel in this role, you should possess a BA/BS in applied quantitative sciences, with a solid foundation in analytics and statistical skills applied in business contexts. Proficiency in machine learning tools and programming languages such as Python or R is essential, along with a demonstrated ability to communicate complex analytical findings effectively. Additionally, a passion for leveraging real-world data and an analytic curiosity to solve complex problems will set you apart as an ideal candidate at GSK.

This guide aims to equip you with the insights and preparation needed to navigate the interview process successfully, ensuring you stand out as a candidate who aligns with GSK's mission and values.

The interview process for a Data Scientist role at GSK is structured and thorough, designed to assess both technical and interpersonal skills. Candidates can expect a multi-step process that includes various types of interviews and assessments.

1. Initial Screening

The process typically begins with an initial screening, which may be conducted via a phone call or video interview with a recruiter. This conversation focuses on your resume, professional background, and motivations for applying to GSK. The recruiter will gauge your fit for the company culture and discuss your relevant experiences, particularly those related to data science projects.

2. Technical Assessment

Following the initial screening, candidates often undergo a technical assessment. This may include online coding tests that evaluate your proficiency in programming languages such as Python or R, as well as your understanding of statistical concepts and machine learning algorithms. Candidates might be asked to solve problems related to data manipulation, analysis, and visualization, often using platforms like HackerRank.

3. Managerial Interview

After successfully completing the technical assessment, candidates typically have a one-on-one interview with the hiring manager or a senior data scientist. This interview delves deeper into your technical skills and past projects, as well as your approach to problem-solving and collaboration. Expect questions that explore your experience with data analysis, machine learning applications, and how you handle challenges in a team setting.

4. Panel Interview

The final stage of the interview process often involves a panel interview, where candidates meet with multiple team members, including data scientists, project managers, and possibly stakeholders from other departments. This round assesses your ability to communicate complex ideas clearly and effectively. You may be asked to present a case study or a project you have worked on, demonstrating your analytical thinking and storytelling skills through data.

5. Behavioral Assessment

Throughout the interview process, GSK places a strong emphasis on behavioral assessments. Expect questions that explore your teamwork, leadership, and conflict resolution skills. The STAR (Situation, Task, Action, Result) method is commonly used to frame these discussions, allowing you to illustrate your experiences in a structured manner.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical expertise and collaborative experiences.

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

Understand the Interview Structure

The interview process at GSK typically involves multiple stages, including an initial screening with a recruiter, followed by interviews with hiring managers and technical panels. Familiarize yourself with this structure and prepare accordingly. Be ready to discuss your resume in detail, focusing on your projects and experiences that align with the role.

Prepare for Technical Assessments

Expect to face technical assessments that may include coding challenges and problem-solving scenarios. Brush up on your skills in SQL, Python, and machine learning concepts. Practice coding problems on platforms like HackerRank or LeetCode, especially those that involve data manipulation and statistical analysis. Be prepared to explain your thought process and the rationale behind your coding decisions during the interview.

Showcase Your Projects

Be ready to discuss your previous projects in depth. Highlight the methodologies you used, the challenges you faced, and the outcomes of your work. GSK values candidates who can demonstrate their ability to apply data science techniques to real-world problems. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey the impact of your contributions clearly.

Emphasize Collaboration and Communication

GSK places a strong emphasis on teamwork and collaboration. Be prepared to discuss how you have worked with cross-functional teams in the past. Highlight your ability to communicate complex technical concepts to non-technical stakeholders. This is particularly important as you may need to collaborate with professionals from various disciplines, such as medical affairs and commercial teams.

Align with Company Values

GSK is committed to creating a diverse and inclusive workplace. Familiarize yourself with their mission and values, and be prepared to discuss how your personal values align with those of the company. Demonstrating a genuine interest in GSK's goals and culture can set you apart from other candidates.

Prepare for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities and how you handle conflict. Reflect on past experiences where you faced challenges and how you overcame them. Use specific examples to illustrate your points, focusing on your analytical skills and decision-making processes.

Be Ready for Situational Judgement Tests

Some candidates have reported taking situational judgement tests as part of the interview process. These tests assess your decision-making skills in hypothetical workplace scenarios. Practice answering situational questions that require you to prioritize tasks, resolve conflicts, or make ethical decisions.

Follow Up Thoughtfully

After your interview, consider sending a thank-you email to express your appreciation for the opportunity to interview. Use this as a chance to reiterate your enthusiasm for the role and the company. Mention any specific points from the interview that resonated with you, reinforcing your interest in joining GSK.

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

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at GSK. The interview process will likely assess your technical skills, problem-solving abilities, and your fit within the company culture. Be prepared to discuss your past projects, your approach to data analysis, and how you can contribute to GSK's mission.

Technical Skills

1. Can you explain a machine learning project you have worked on and the impact it had?

This question aims to assess your practical experience with machine learning and your ability to communicate its significance.

How to Answer

Discuss the project’s objectives, the data you used, the algorithms implemented, and the results achieved. Highlight how your work contributed to decision-making or improved processes.

Example

“I worked on a project to predict patient outcomes using electronic health records. I utilized logistic regression and random forests to analyze the data, which led to a 20% improvement in our predictive accuracy. This model helped the medical team prioritize interventions for high-risk patients, ultimately improving patient care.”

2. What is your experience with data visualization tools, and how do you use them to communicate findings?

This question evaluates your ability to present data insights effectively.

How to Answer

Mention specific tools you have used (e.g., Tableau, Power BI) and describe how you tailor visualizations to your audience.

Example

“I frequently use Tableau to create interactive dashboards that allow stakeholders to explore data trends. For instance, I developed a dashboard for our marketing team that visualized customer segmentation, which helped them tailor their campaigns more effectively.”

3. Describe your experience with SQL and how you have used it in your projects.

This question tests your database management skills and your ability to extract insights from data.

How to Answer

Provide examples of complex queries you have written and how they contributed to your analysis.

Example

“I have extensive experience with SQL, including writing complex joins and subqueries to extract data from multiple tables. In one project, I wrote a query that aggregated sales data across different regions, which allowed us to identify underperforming areas and adjust our strategy accordingly.”

4. How do you approach feature selection in your models?

This question assesses your understanding of model optimization and data preprocessing.

How to Answer

Discuss techniques you use for feature selection and why they are important for model performance.

Example

“I typically use techniques like recursive feature elimination and LASSO regression to identify the most impactful features. This not only improves model accuracy but also reduces overfitting, making the model more generalizable.”

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

This question tests your foundational knowledge of machine learning concepts.

How to Answer

Clearly define both terms and provide examples of each.

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 goal is to find hidden patterns, such as clustering customers based on purchasing behavior.”

Behavioral Questions

1. Describe a time when you had to resolve a conflict within a team.

This question evaluates your interpersonal skills and ability to work collaboratively.

How to Answer

Use the STAR method (Situation, Task, Action, Result) to structure your response.

Example

“In a previous project, there was a disagreement between team members about the direction of our analysis. I facilitated a meeting where everyone could voice their concerns and preferences. By encouraging open communication, we reached a consensus on a hybrid approach that combined both ideas, leading to a successful project outcome.”

2. How do you prioritize your work when you have multiple projects with tight deadlines?

This question assesses your time management and organizational skills.

How to Answer

Discuss your approach to prioritization and any tools or methods you use.

Example

“I prioritize my tasks based on urgency and impact. I use project management tools like Trello to keep track of deadlines and progress. For instance, when faced with multiple deadlines, I assess which project aligns most closely with business goals and allocate my time accordingly.”

3. Why do you want to work for GSK?

This question gauges your motivation and alignment with the company’s values.

How to Answer

Express your interest in GSK’s mission and how your skills align with their goals.

Example

“I admire GSK’s commitment to improving global health through innovative solutions. As a data scientist, I am excited about the opportunity to leverage data to drive impactful decisions in medical affairs, ultimately contributing to better patient outcomes.”

4. How do you stay current with advancements in data science and machine learning?

This question evaluates your commitment to continuous learning.

How to Answer

Mention specific resources, courses, or communities you engage with to stay updated.

Example

“I regularly attend webinars and workshops, and I’m an active member of several data science forums. I also follow leading researchers on platforms like Twitter and read journals to keep up with the latest methodologies and technologies.”

5. Can you give an example of how you used data to influence a business decision?

This question assesses your ability to translate data insights into actionable business strategies.

How to Answer

Describe a specific instance where your analysis led to a significant decision.

Example

“In my last role, I analyzed customer feedback data to identify key pain points in our product. I presented my findings to the product team, which led to a redesign that improved user satisfaction scores by 30% within three months.”