Digitas Data Scientist Interview Questions + Guide in 2025

Digitas is the Connected Marketing agency, committed to creating better connections between brands and people through innovative strategies that leverage data, technology, and creativity.

As a Data Scientist at Digitas, you will play a crucial role in delivering analytic solutions that drive strategic growth for various clients. Your responsibilities will encompass building inferential and predictive models, utilizing machine learning algorithms, and processing big data using distributed systems and customer data pipelines. You will be tasked with addressing complex marketing and business challenges, which could range from cross-channel media optimization to customer experience enhancement and business strategy development.

To excel in this role, you will need to have strong analytical skills, particularly in SQL and analytics, as these are critical for mining and interpreting data effectively. A solid foundation in statistics and experience with product metrics will further enhance your ability to derive insights and translate them into compelling narratives that resonate with clients. Additionally, having a collaborative mindset and the ability to communicate complex analytical concepts to both technical and non-technical audiences are essential traits that align with Digitas's values.

This guide aims to prepare you for your interview by equipping you with a clear understanding of the role and the skills necessary to succeed at Digitas. A focused approach to your preparation will help you stand out as a strong candidate in this competitive environment.

The interview process for a Data Scientist at Digitas is structured to assess both technical skills and cultural fit within the organization. It typically consists of several key stages, each designed to evaluate different aspects of a candidate's qualifications and alignment with the company's values.

1. Initial Recruiter Call

The process begins with a 30-minute phone interview with a recruiter. This initial call serves as an opportunity for the recruiter to gauge your interest in the role and the company, as well as to discuss your background, skills, and career aspirations. The recruiter will also provide insights into the company culture and the expectations for the Data Scientist position.

2. Hiring Manager Interview

Following the recruiter call, candidates will have a conversation with the hiring manager. This interview usually lasts around 30 minutes and focuses on your experience and how it aligns with the specific needs of the team. Expect to discuss your previous projects, particularly those that involved data analysis, SQL, and machine learning, as well as your approach to solving complex business problems.

3. Technical Assessment

The next step involves a technical interview, which may include a coding challenge or a case study. Candidates are typically asked to demonstrate their proficiency in SQL and analytics, as well as their ability to apply statistical methods and machine learning techniques to real-world scenarios. This assessment is crucial for evaluating your technical capabilities and problem-solving skills.

4. Presentation of Work

Candidates may be required to prepare a presentation based on a previous project or a hypothetical case study. This presentation should highlight your analytical approach, the tools and methodologies used, and the outcomes achieved. The goal is to showcase your ability to communicate complex data insights effectively to both technical and non-technical audiences.

5. Team Fit Interview

The final stage of the interview process involves a discussion with potential team members. This round focuses on assessing your fit within the team and the broader company culture. Expect questions that explore your collaborative skills, how you handle feedback, and your approach to sharing knowledge and learning from others.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may arise in each of these stages.

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

Understand the Interview Process

The interview process at Digitas typically consists of multiple stages, including an initial recruiter call, a conversation with the hiring manager, a technical interview, and a presentation of your work. Familiarize yourself with this structure and prepare accordingly. Be ready to discuss your previous projects, particularly those that involved machine learning, SQL, and data analysis, as these are crucial for the role.

Showcase Your Technical Skills

Given the emphasis on SQL and analytics in this role, ensure you are well-versed in these areas. Brush up on your SQL skills, focusing on complex queries, data manipulation, and integration techniques. Additionally, be prepared to discuss your experience with data analysis and statistical methods. Highlight any projects where you successfully applied these skills to solve real-world problems.

Prepare for Problem-Solving Challenges

Expect to face challenges that require you to demonstrate your analytical thinking and problem-solving abilities. You may be asked to translate marketing and business questions into analytical plans or to conduct exploratory data analysis. Practice articulating your thought process clearly and logically, as this will be key in showcasing your ability to tackle complex data problems.

Communicate Effectively

Digitas values the ability to communicate complex analytical concepts to both technical and non-technical audiences. During your interview, focus on how you present your findings and insights. Use storytelling techniques to make your data-driven conclusions relatable and impactful. Be prepared to summarize your analytical processes and results succinctly.

Emphasize Collaboration and Stakeholder Engagement

Collaboration is a significant aspect of the role, as you will be working with various internal and external stakeholders. Highlight your experience in team settings and your ability to establish clear analytical objectives and timelines. Discuss how you have successfully collaborated with others to achieve common goals, particularly in data-driven projects.

Demonstrate Your Passion for Marketing Analytics

Showcase your enthusiasm for marketing analytics and how it drives your work. Be prepared to discuss any relevant experiences or projects that illustrate your interest in this area. This could include specific campaigns you’ve worked on or insights you’ve derived that had a measurable impact on business outcomes.

Be a Self-Starter

Digitas thrives on self-starters who can navigate a fast-paced environment. Share examples of how you have taken initiative in your previous roles, whether through leading projects, proposing new ideas, or learning new skills independently. This will demonstrate your proactive nature and fit within the company culture.

Prepare Thoughtful Questions

Finally, come prepared with insightful questions about the team, the projects you might work on, and the company’s approach to data science. This not only shows your interest in the role but also helps you assess if Digitas is the right fit for you. Tailor your questions to reflect your understanding of the company’s goals and challenges.

By following these tips, you will be well-prepared to make a strong impression during your interview at Digitas. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Digitas. The interview process will likely focus on your ability to analyze data, apply statistical methods, and communicate insights effectively. Be prepared to demonstrate your technical skills, particularly in SQL, analytics, and statistics, as well as your understanding of marketing applications.

SQL and Data Manipulation

1. Can you explain how you would optimize a SQL query for better performance?

Optimizing SQL queries is crucial for handling large datasets efficiently. Discuss techniques such as indexing, avoiding SELECT *, and using JOINs appropriately.

Example

"I would start by analyzing the execution plan to identify bottlenecks. Then, I would consider adding indexes on columns frequently used in WHERE clauses or JOIN conditions. Additionally, I would avoid using SELECT * and instead specify only the necessary columns to reduce the amount of data processed."

2. Describe a complex SQL query you have written and the problem it solved.

This question assesses your practical experience with SQL. Be specific about the problem and how your query addressed it.

Example

"I once wrote a complex SQL query to analyze customer purchase patterns over time. By joining multiple tables and using window functions, I was able to calculate the average purchase value per customer segment, which helped the marketing team tailor their campaigns effectively."

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

Handling missing data is a common challenge in data analysis. Discuss various strategies such as imputation, removal, or using algorithms that support missing values.

Example

"I typically assess the extent of missing data first. If it's minimal, I might impute values using the mean or median. For larger gaps, I consider removing those records or using models that can handle missing values, ensuring that the integrity of the analysis is maintained."

4. What are some common SQL functions you use for data analysis?

This question tests your familiarity with SQL functions. Mention aggregate functions, window functions, and any specific functions relevant to your analysis.

Example

"I frequently use aggregate functions like COUNT, SUM, and AVG for summarizing data. Additionally, I utilize window functions such as ROW_NUMBER() and RANK() to analyze trends over time, which is particularly useful in customer behavior analysis."

Analytics and Statistics

1. Explain the difference between supervised and unsupervised learning.

Understanding these concepts is fundamental in data science. Be clear about the definitions and provide examples.

Example

"Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting sales based on historical data. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like customer segmentation based on purchasing behavior."

2. How do you determine which statistical test to use for a given dataset?

This question assesses your statistical knowledge. Discuss factors like data type, distribution, and research questions.

Example

"I consider the type of data I have—categorical or continuous—and the distribution of the data. For instance, if I want to compare means between two groups, I would use a t-test, while for more than two groups, I would opt for ANOVA. Additionally, I check assumptions like normality and homogeneity of variance before selecting the test."

3. Can you describe a time when you used statistical analysis to influence a business decision?

This question evaluates your ability to apply statistics in a business context. Share a specific example with measurable outcomes.

Example

"In a previous role, I conducted a regression analysis to understand the impact of marketing spend on sales. The results showed a significant return on investment for digital campaigns, which led to an increase in budget allocation for those channels, resulting in a 20% increase in sales over the next quarter."

4. What is A/B testing, and how would you implement it?

A/B testing is a common method in marketing analytics. Explain the process and its importance.

Example

"A/B testing involves comparing two versions of a webpage or campaign to see which performs better. I would define clear metrics for success, randomly assign users to each version, and analyze the results using statistical tests to determine if the differences are significant before making a decision."

Machine Learning and Predictive Modeling

1. Describe a machine learning project you have worked on. What was your approach?

This question allows you to showcase your hands-on experience. Discuss the problem, your methodology, and the outcome.

Example

"I worked on a project to predict customer churn using logistic regression. I started by cleaning the data and selecting relevant features. After training the model, I evaluated its performance using ROC-AUC scores, which helped the client implement targeted retention strategies, reducing churn by 15%."

2. How do you evaluate the performance of a machine learning model?

Understanding model evaluation is key. Discuss metrics and validation techniques.

Example

"I evaluate model performance using metrics like accuracy, precision, recall, and F1 score, depending on the problem type. I also use cross-validation to ensure the model generalizes well to unseen data, which is crucial for maintaining reliability in predictions."

3. What techniques do you use for feature selection?

Feature selection is vital for model performance. Discuss methods you are familiar with.

Example

"I often use techniques like recursive feature elimination and LASSO regression for feature selection. Additionally, I analyze feature importance scores from tree-based models to identify which features contribute most to the model's predictive power."

4. Can you explain overfitting and how to prevent it?

Overfitting is a common issue in machine learning. Discuss its implications and prevention strategies.

Example

"Overfitting occurs when a model learns noise in the training data rather than the underlying pattern, leading to poor performance on new data. To prevent it, I use techniques like cross-validation, regularization, and pruning in decision trees, ensuring the model remains generalizable."