Known Data Scientist Interview Questions + Guide in 2025

Known is a modern marketing company that seamlessly blends creativity and analytics to tackle the challenges facing marketers today.

As a Data Scientist at Known, you will play a crucial role in the Media Science team, which specializes in leveraging advanced analytics, machine learning, and statistical methodologies to optimize advertising strategies and drive measurable business outcomes. Key responsibilities include collaborating with media strategists and clients to translate business goals into actionable metrics, conducting quantitative analysis, and developing experimental designs for campaign optimization. Proficiency in Python and SQL is essential, as you will be expected to perform data manipulation, statistical modeling, and produce reproducible analyses.

A great fit for this role will possess a strong foundation in statistics and analytical reasoning, coupled with excellent communication skills to convey complex technical concepts to both technical and non-technical stakeholders. You should be a proactive problem-solver, demonstrating a passion for managing data quality and accuracy while delivering results on multiple projects under tight deadlines. This guide will help you prepare effectively for your interview by highlighting the skills and knowledge areas that are most relevant to the Data Scientist position at Known.

The interview process for a Data Scientist role at Known is structured to assess both technical skills and cultural fit within the organization. It typically consists of several key stages:

1. Initial Recruiter Call

The process begins with a preliminary call with a recruiter. This conversation is designed to gauge your interest in the role and the company, as well as to discuss your background and experience. The recruiter will also provide an overview of the position and the expectations for the role. While this call is generally friendly, it is important to be prepared to discuss your qualifications and how they align with Known's values and mission.

2. Technical Screen

Following the initial call, candidates typically undergo a technical screening. This may involve a coding interview that focuses on your proficiency in Python and SQL, as well as your ability to solve analytical problems. Expect to encounter questions that assess your understanding of data manipulation, statistical analysis, and possibly some machine learning concepts. The technical screen is often time-constrained, so practice coding under pressure to ensure you can demonstrate your skills effectively.

3. Analytical and Problem-Solving Assessment

Candidates may then be presented with analytical brain teasers or case study questions that test your mathematical reasoning and problem-solving abilities. This stage is crucial for demonstrating your analytical thinking and how you approach complex data-related challenges. Be prepared to explain your thought process clearly, as communication is key in this role.

4. Behavioral Interview

The next step usually involves a behavioral interview with a manager or team lead. This interview focuses on your past experiences, how you handle challenges, and your approach to teamwork and collaboration. Expect questions that explore your ability to communicate technical concepts to non-technical stakeholders and your experience in managing client relationships. This is also an opportunity to showcase your alignment with Known's culture and values.

5. Final Interview

The final stage often includes a more in-depth discussion with senior management or the hiring manager. This interview may cover both technical and behavioral aspects, delving deeper into your experience and how you can contribute to the team. You may also be asked to present a project or analysis you have worked on, highlighting your ability to translate data insights into actionable business strategies.

As you prepare for your interview, keep in mind the types of questions that may arise in each of these stages, particularly those related to your technical skills and your ability to work collaboratively in a fast-paced environment.

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

Prepare for a Multi-Faceted Interview Process

The interview process at Known typically involves several stages, including a recruiter call, technical assessments, and behavioral interviews. Familiarize yourself with the structure and prepare accordingly. Expect to encounter math and analytical brain teasers, coding challenges in Python and SQL, and discussions around machine learning concepts. Given the emphasis on technical skills, ensure you can articulate your thought process clearly during these assessments.

Brush Up on Key Technical Skills

Given the role's focus on data science, it's crucial to have a strong command of SQL and Python, as these are the primary tools you will be using. Practice coding problems that involve SQL joins, data manipulation with pandas, and Python list comprehensions. Additionally, be prepared to discuss statistical concepts and machine learning models, as these will likely come up during the technical interviews.

Communicate Clearly and Confidently

Known values superb communication skills, especially the ability to convey complex technical information to both technical and non-technical stakeholders. Practice explaining your past projects and technical concepts in layman's terms. This will not only demonstrate your expertise but also your ability to connect with clients and team members effectively.

Understand the Company Culture

Known emphasizes collaboration, continuous learning, and innovation. Familiarize yourself with their core values: being one team, seeing the good, never stopping learning, and always seeking a better way. During your interview, reflect these values in your responses and demonstrate how you embody them in your work. This alignment will resonate well with the interviewers.

Prepare for Behavioral Questions

Expect behavioral questions that assess your fit within the company culture and your ability to work in a team. Be ready to share examples from your past experiences that highlight your problem-solving skills, adaptability, and how you handle challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses effectively.

Anticipate Changes in Job Expectations

Be aware that the role's expectations may evolve throughout the interview process. Some candidates have reported discrepancies between the job description and the actual responsibilities discussed during interviews. Stay flexible and open-minded, and be prepared to ask clarifying questions about the role to ensure it aligns with your career goals.

Practice Problem-Solving Under Pressure

Given the emphasis on analytical thinking, practice solving brain teasers and technical problems under timed conditions. This will help you become comfortable with the pressure of the interview environment and improve your ability to think critically on the spot.

Follow Up Professionally

After your interview, send a thank-you email to express your appreciation for the opportunity to interview. This not only shows professionalism but also reinforces your interest in the position. If you have any lingering questions about the role or the company, this is a good time to ask.

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

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Known. The interview process will likely assess your technical skills in data analysis, machine learning, and statistical modeling, as well as your ability to communicate complex concepts effectively. Be prepared to demonstrate your analytical reasoning and problem-solving skills, particularly in the context of media measurement and campaign optimization.

Technical Skills

1. Can you explain the concept of regression analysis and its applications in data science?

Understanding regression is crucial for analyzing relationships between variables and making predictions.

How to Answer

Discuss the types of regression (linear, logistic, etc.) and provide examples of how you have applied regression analysis in past projects.

Example

“Regression analysis allows us to model the relationship between a dependent variable and one or more independent variables. For instance, I used linear regression to predict customer spending based on their demographic data, which helped the marketing team tailor their campaigns effectively.”

2. What are some common SQL functions you use for data manipulation?

SQL is essential for querying and managing data in databases.

How to Answer

Mention specific SQL functions you are familiar with, such as JOINs, GROUP BY, and aggregate functions, and provide examples of how you have used them.

Example

“I frequently use JOINs to combine data from multiple tables, along with aggregate functions like COUNT and SUM to summarize data. For example, I created a report that combined sales data with customer demographics to analyze purchasing trends.”

3. Describe a machine learning model you have built and the process you followed.

This question assesses your practical experience with machine learning.

How to Answer

Outline the steps you took, from data collection and preprocessing to model selection and evaluation.

Example

“I built a decision tree model to classify customer churn. I started by cleaning the dataset, then selected features based on their importance. After training the model, I evaluated its performance using cross-validation and adjusted hyperparameters to improve accuracy.”

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

Handling missing data is a critical skill in data analysis.

How to Answer

Discuss various techniques for dealing with missing data, such as imputation or removal, and when to use each method.

Example

“I typically assess the extent of missing data first. If it’s minimal, I might use mean imputation. However, if a significant portion is missing, I would consider removing those records or using more advanced techniques like KNN imputation to preserve the dataset's integrity.”

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

This question tests your foundational knowledge of machine learning concepts.

How to Answer

Define both terms and provide examples of algorithms used in each category.

Example

“Supervised learning involves training a model on labeled data, such as using logistic regression for classification tasks. In contrast, unsupervised learning deals with unlabeled data, like clustering customers using K-means to identify distinct segments.”

Statistics and Probability

1. What is the Central Limit Theorem and why is it important?

Understanding statistical principles is key for data analysis.

How to Answer

Explain the theorem and its implications for sampling distributions.

Example

“The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial for making inferences about population parameters based on sample data.”

2. How would you approach a problem involving conditional probability?

This question assesses your understanding of probability concepts.

How to Answer

Provide a clear explanation of conditional probability and how you would apply it to a specific scenario.

Example

“I would use Bayes’ theorem to calculate conditional probabilities. For instance, if I wanted to determine the probability of a customer purchasing a product given they clicked on an ad, I would analyze the relevant data to compute the likelihood.”

3. Can you describe a time when you used statistical analysis to solve a business problem?

This question evaluates your practical application of statistics.

How to Answer

Share a specific example where statistical analysis led to actionable insights.

Example

“I conducted a statistical analysis to determine the effectiveness of a marketing campaign. By comparing conversion rates before and after the campaign using hypothesis testing, I was able to demonstrate a significant increase in sales, which justified further investment in similar strategies.”

4. What is the significance of p-values in hypothesis testing?

Understanding hypothesis testing is essential for data-driven decision-making.

How to Answer

Explain what p-values represent and their role in determining statistical significance.

Example

“A p-value indicates the probability of observing the data, or something more extreme, if the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, which is crucial for validating our findings in experiments.”

5. How do you assess the quality of a dataset before analysis?

Data quality is vital for accurate analysis.

How to Answer

Discuss the criteria you use to evaluate data quality, such as completeness, consistency, and accuracy.

Example

“I assess data quality by checking for missing values, duplicates, and outliers. I also validate the data against known benchmarks to ensure its accuracy before proceeding with any analysis.”

Behavioral and Situational Questions

1. Describe a challenging project you worked on and how you overcame obstacles.

This question evaluates your problem-solving and teamwork skills.

How to Answer

Share a specific project, the challenges faced, and the strategies you employed to overcome them.

Example

“I worked on a project where the data was incomplete, which hindered our analysis. I collaborated with the data engineering team to identify gaps and implemented a data collection strategy that improved our dataset, allowing us to complete the analysis successfully.”

2. How do you prioritize tasks when managing multiple projects?

This question assesses your project management skills.

How to Answer

Discuss your approach to prioritization and time management.

Example

“I prioritize tasks based on deadlines and the impact on business objectives. I use project management tools to track progress and ensure that I allocate time effectively to meet all project requirements.”

3. How do you communicate complex technical information to non-technical stakeholders?

Effective communication is key in a consulting role.

How to Answer

Explain your strategy for simplifying complex concepts for diverse audiences.

Example

“I focus on using clear, jargon-free language and visual aids like charts and graphs to illustrate key points. I also encourage questions to ensure understanding and foster a collaborative environment.”

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

This question evaluates your impact on business strategy.

How to Answer

Share a specific instance where your data analysis led to a significant business decision.

Example

“I analyzed customer feedback data to identify pain points in our service. By presenting these insights to the management team, we implemented changes that improved customer satisfaction scores by 20% within three months.”

5. Where do you see yourself in five years?

This question gauges your career aspirations and alignment with the company’s goals.

How to Answer

Discuss your professional goals and how they align with the company’s mission.

Example

“In five years, I see myself in a leadership role within the data science team, driving innovative projects that leverage data to enhance client outcomes. I am excited about the opportunity to grow with Known and contribute to its success.”