Relativity Data Scientist Interview Questions + Guide in 2025

Relativity is a leader in utilizing AI technologies to transform the eDiscovery process, helping legal professionals swiftly and efficiently organize vast amounts of data to uncover the truth.

The Data Scientist role at Relativity involves developing machine-learning models and algorithms tailored for the eDiscovery industry. Key responsibilities include collaborating across teams to create production-level code, contributing to the lifecycle of model deployment, and synthesizing datasets for evaluation. A strong candidate should possess a blend of technical expertise in machine learning and programming, particularly in Python, alongside an understanding of the legal industry. Effective communication skills and the ability to employ inclusive language are crucial, as the role involves conveying complex data science concepts to diverse stakeholders. Ideal candidates will thrive in a culture of experimentation and innovation, embodying Relativity's commitment to responsible AI practices.

This guide is designed to enhance your preparation for the interview process by providing insights into the role's expectations and the skills that will be evaluated.

The interview process for a Data Scientist role at Relativity is structured to assess both technical skills and cultural fit within the organization. Candidates can expect a multi-step process that includes various types of interviews, focusing on their experience, problem-solving abilities, and alignment with the company's values.

1. Initial Phone Screen

The process typically begins with a phone interview conducted by a recruiter or hiring manager. This initial conversation lasts about 30 minutes and serves as an opportunity for the interviewer to gauge the candidate's background, experience, and interest in the role. Expect questions about your resume, previous projects, and how your skills align with the position. This stage may also include a discussion about the company culture and values to assess fit.

2. Technical Assessment

Following the initial screen, candidates are often required to complete a technical assessment, which may be conducted through platforms like HackerRank. This assessment usually consists of coding challenges that test your knowledge of algorithms, data structures, and programming languages such as Python. Candidates should be prepared for questions that require a solid understanding of object-oriented programming and basic machine learning concepts.

3. Onsite Interviews

Candidates who successfully pass the technical assessment are invited for onsite interviews, which can last several hours and typically consist of multiple rounds. These rounds may include:

• Technical Interviews: Conducted by engineers or data scientists, these sessions focus on problem-solving and coding skills. Candidates may be asked to solve algorithmic problems on a whiteboard or through a coding platform, demonstrating their thought process and technical proficiency.

• Behavioral Interviews: These interviews assess how candidates align with Relativity's core values and culture. Expect questions about past experiences, teamwork, and how you handle challenges in a work environment. Interviewers may ask for specific examples that illustrate your problem-solving abilities and communication skills.

• Collaborative Problem-Solving: Some interviews may involve collaborative exercises where candidates work with interviewers to solve a problem, showcasing their ability to communicate and work effectively in a team setting.

4. Final Discussions

In some cases, candidates may have a final discussion with a senior manager or team lead. This conversation often focuses on the candidate's long-term career goals, their interest in the legal industry, and how they envision contributing to the team and the company's mission.

Throughout the interview process, candidates should be prepared to discuss their technical skills, past experiences, and how they can contribute to Relativity's commitment to responsible AI and innovation in the eDiscovery industry.

Next, let's explore the specific interview questions that candidates have encountered during the process.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Relativity. Candidates should focus on demonstrating their technical expertise, problem-solving abilities, and understanding of machine learning concepts, as well as their fit with the company's values and culture.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial. Be prepared to discuss examples of each type and when to use them.

How to Answer

Clearly define both terms and provide examples of algorithms used in each category. Discuss scenarios where one might be preferred over the other.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as classification tasks using algorithms like logistic regression. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering with K-means.”

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

Outline the project scope, your role, the challenges encountered, and how you overcame them. Highlight any innovative solutions you implemented.

Example

“I worked on a project to predict customer churn using historical data. One challenge was dealing with imbalanced classes. I implemented SMOTE to generate synthetic samples for the minority class, which improved our model's accuracy significantly.”

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

This question tests your knowledge of model evaluation metrics.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

“I evaluate model performance using multiple metrics. For classification tasks, I focus on precision and recall to understand the trade-offs, especially in imbalanced datasets. I also use ROC-AUC to assess the model's ability to distinguish between classes.”

4. What is overfitting, and how can you prevent it?

Understanding overfitting is essential for building robust models.

How to Answer

Define overfitting and discuss techniques to prevent it, such as cross-validation, regularization, and pruning.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern. To prevent it, I use techniques like cross-validation to ensure the model generalizes well, and I apply regularization methods like L1 and L2 to penalize overly complex models.”

Statistics & Probability

1. Explain the Central Limit Theorem and its significance.

This question assesses your understanding of statistical concepts.

How to Answer

Define the Central Limit Theorem and explain its implications for sampling distributions.

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 population's distribution. This is significant because it allows us to make inferences about population parameters using sample statistics.”

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

This question evaluates your data preprocessing skills.

How to Answer

Discuss various strategies for handling missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

“I handle missing data by first analyzing the extent and pattern of the missingness. Depending on the situation, I might use mean or median imputation for numerical data or mode for categorical data. If the missing data is substantial, I may consider using algorithms that can handle missing values directly.”

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

Understanding hypothesis testing is crucial for data scientists.

How to Answer

Define both types of errors and provide examples of their implications in real-world scenarios.

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. For instance, in a medical test, a Type I error could mean falsely diagnosing a disease, while a Type II error could mean missing a diagnosis.”

Algorithms

1. Can you explain how a decision tree works?

This question tests your knowledge of algorithms used in machine learning.

How to Answer

Describe the structure of a decision tree and how it makes decisions based on feature values.

Example

“A decision tree splits the data into subsets based on feature values, creating branches for each possible outcome. It continues to split until it reaches a stopping criterion, such as a maximum depth or minimum samples per leaf. This model is intuitive and easy to interpret.”

2. What is the purpose of cross-validation?

This question assesses your understanding of model validation techniques.

How to Answer

Explain the concept of cross-validation and its role in assessing model performance.

Example

“Cross-validation is used to evaluate a model's performance by partitioning the data into training and testing sets multiple times. This helps ensure that the model generalizes well to unseen data and reduces the risk of overfitting.”

3. Describe the concept of feature engineering and its importance.

This question evaluates your understanding of data preparation techniques.

How to Answer

Discuss the process of feature engineering and how it can impact model performance.

Example

“Feature engineering involves creating new features or modifying existing ones to improve model performance. It’s crucial because the right features can significantly enhance a model's ability to learn patterns in the data, leading to better predictions.”

Behavioral Questions

1. Describe a time when you had to work with a difficult team member. How did you handle it?

This question assesses your interpersonal skills and ability to work in a team.

How to Answer

Provide a specific example, focusing on your approach to resolving the conflict and maintaining a productive working relationship.

Example

“I once worked with a team member who was resistant to feedback. I scheduled a one-on-one meeting to understand their perspective and shared my concerns constructively. This open dialogue helped us find common ground and improved our collaboration.”

2. How do you prioritize your tasks when working on multiple projects?

This question evaluates your time management skills.

How to Answer

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

Example

“I prioritize tasks based on deadlines and project impact. I use tools like Trello to visualize my workload and ensure I allocate time effectively. Regular check-ins with my team also help me adjust priorities as needed.”

3. Why do you want to work at Relativity?

This question assesses your fit with the company culture and values.

How to Answer

Express your interest in the company’s mission and how your values align with theirs.

Example

“I admire Relativity’s commitment to responsible AI and its focus on improving the justice system. I’m passionate about using technology to make a positive impact, and I believe my skills in machine learning can contribute to your innovative projects.”