Samsung Ads Data Scientist interview questions & guide 2026

The interview process is designed to test your ability to apply data science principles to real-world business scenarios. While questions vary by team, the following patterns reflect the core competencies required for the Data Scientist role.

SQL and Data Manipulation

These questions test your proficiency in extracting and transforming data from large-scale databases, a daily requirement for this role.

• How would you use SQL window functions to calculate a rolling 7-day average of ad impressions?
• Explain how you would handle missing values in a large dataset before performing an analysis.

Preparation for Samsung Ads should be structured around demonstrating both depth of technical knowledge and breadth of business awareness. Your goal is to show that you are not just a model-builder, but a partner in the product development lifecycle.

Role-related knowledge – You must demonstrate mastery over the core tools of the trade, specifically SQL and statistical methodologies. Interviewers will look for your ability to explain why you chose a specific technique rather than just how you implemented it.

Problem-solving ability – You will be presented with ambiguous scenarios that require a structured approach. Practice breaking down large, vague problems into smaller, testable hypotheses and defining the metrics that will guide your decision-making.

Communication and Leadership – At Samsung Ads, your ability to influence depends on your communication. Practice translating technical concepts into simple, business-oriented language that highlights the potential ROI or risk mitigation of your work.

Culture fit – Samsung Ads values collaboration and ownership. Be prepared to discuss your past projects with a focus on your individual contributions, how you handled setbacks, and how you supported your team to achieve collective goals.

The interview process at Samsung Ads is rigorous and highly focused on practical application. Candidates typically progress through a structured loop that begins with an initial screening, followed by a technical assessment, and culminates in multiple rounds of virtual interviews. The pace is generally efficient, and you should be prepared to discuss your past projects in significant detail.

The company prioritizes candidates who can demonstrate a "builder" mindset. You can expect the interviews to be less about abstract theory and more about how you apply machine learning, statistics, and SQL to solve actual business problems. The process is designed to evaluate your technical competency, your ability to handle data-heavy tasks, and your cultural alignment with a fast-moving, global organization.

This visual timeline tracks your progression from the technical assessment through the final behavioral and case-study rounds. Use this to pace your preparation, ensuring you have enough time to review both your foundational technical skills and your past project experiences. Remember that variations may occur depending on the specific team or seniority level of the role.

Experimentation and Statistics

This is a critical area for Samsung Ads. You will be evaluated on your ability to design tests that are both statistically sound and practically executable.

• Statistical significance – Understanding p-values, confidence intervals, and power analysis.
• A/B testing – Designing experiments that avoid bias and account for external factors.
• Experimentation pitfalls – Recognizing selection bias, network effects, and Simpson’s paradox.

As a Data Scientist, your primary responsibility is to drive product performance through data. You will spend a significant portion of your time performing exploratory data analysis to uncover trends in user behavior and ad efficacy. This involves working with massive datasets to build models that predict user intent and optimize ad delivery.

You will act as the data expert for your product team. This means you are responsible for defining key performance indicators (KPIs) and designing experiments to test new features. Collaboration is key; you will frequently work with engineers to ensure data pipelines are robust and with product managers to ensure your insights are aligned with the company's strategic goals. You are expected to be the voice of data in every room you enter.

To be competitive for this role, you must demonstrate a strong foundation in both the engineering and analytical aspects of data science.

• Technical skills – Proficiency in SQL (including window functions), Python or R, and experience with machine learning libraries. You should also be comfortable with A/B testing frameworks and statistical analysis.

• Experience – Prior experience in the advertising technology (AdTech) space is highly valued but not strictly required. A track record of owning end-to-end data projects is essential.

• Soft skills – Strong verbal and written communication skills are non-negotiable. You must be able to influence stakeholders and work effectively in a cross-functional, global environment.

• Must-have – Experience with large-scale data manipulation, deep knowledge of statistics, and a proven ability to design and analyze experiments.

• Nice-to-have – Experience with cloud platforms, familiarity with Computer Vision or image processing (as seen in some technical assessments), and experience working with distributed systems.

Q: How much time should I dedicate to preparing for the technical assessment? A: Dedicate at least two weeks of focused preparation. The test is comprehensive, so ensure you are comfortable with both SQL queries and applying machine learning to real-world use cases.

Q: What is the most important trait for a successful candidate? A: The ability to connect data to business outcomes. Being a "technical expert" is not enough; you must be able to explain how your analysis moves the needle for Samsung Ads.

Q: How are behavioral questions weighted? A: They are a critical part of the process. Even the most technically gifted candidate will not pass if they cannot demonstrate leadership, conflict resolution, and a collaborative mindset.

Q: Is there a focus on specific machine learning models? A: The focus is on knowing which model to use for a specific use case and why. Understand the trade-offs between supervised and unsupervised learning, and be prepared to discuss model evaluation metrics.

• Structure your answers: Use the STAR method (Situation, Task, Action, Result) for behavioral questions to keep your responses concise and impactful.
• Think aloud: During technical sessions, explain your thought process. Interviewers are often more interested in how you approach a problem than the final code itself.
• Know your CV: Be prepared to dive deep into any project listed on your resume. You should be able to explain the "why" behind every technical decision you made.
• Ask clarifying questions: If a case study feels ambiguous, ask questions to narrow the scope. This shows you are a thoughtful problem solver.

The Data Scientist role at Samsung Ads is an exceptional opportunity to influence a global advertising platform at scale. Success in this role requires a balanced approach: you must be technically precise in your analysis while remaining strategically focused on the product’s business objectives. By mastering the core competencies of experimentation, SQL-based data manipulation, and clear communication, you will be well-positioned to succeed in your interviews.

You can explore additional interview insights, practice questions, and preparation resources on Dataford to sharpen your skills. Remember that every interview is a chance to showcase your problem-solving process—approach each question with curiosity and confidence.

The provided compensation data reflects the current market range for this position. Candidates should interpret these figures as a starting point, keeping in mind that total compensation packages often include base salary, performance-based bonuses, and equity, which may vary based on experience level and location.