New Relic Data Scientist Interview Questions + Guide in 2025

New Relic is a leader in the Observability industry, pioneering innovative AI and machine learning solutions to help organizations monitor and optimize their digital performance.

As a Data Scientist at New Relic, you will play a critical role in developing machine learning solutions that address complex customer challenges through the analysis of large datasets, both structured and unstructured. Key responsibilities include conducting experiments with various ML techniques, prototyping end-to-end ML solutions, performing data analysis to identify trends, and designing A/B tests. Ideal candidates should possess a strong foundation in machine learning methodologies, proficiency in programming languages such as Python, and experience with data visualization tools. Additionally, the ability to communicate data-driven insights effectively and translate business needs into actionable analytical initiatives is essential.

This guide will equip you with a deeper understanding of the role's expectations and help you prepare for common interview questions, ultimately enhancing your chances of success in securing a position with New Relic.

The interview process for a Data Scientist role at New Relic is structured and thorough, designed to assess both technical skills and cultural fit. Here’s a breakdown of the typical steps involved:

1. Initial Phone Screen

The process begins with a phone screening conducted by a recruiter. This initial conversation typically lasts around 30 minutes and focuses on your background, experience, and motivations for applying to New Relic. The recruiter will also provide an overview of the role and the company culture, allowing you to ask any preliminary questions.

2. Technical Interview with Hiring Manager

Following the initial screen, candidates usually have a technical interview with the hiring manager. This session is more focused on your technical skills and may include basic coding questions or problem-solving scenarios relevant to the role. It’s an opportunity for the hiring manager to gauge your fit for the team and discuss the specifics of the position.

3. Take-Home Coding Challenge

Candidates who perform well in the previous rounds are typically given a take-home coding challenge. This assignment can vary in complexity and may require several hours to complete. The challenge is designed to assess your coding skills, problem-solving abilities, and understanding of machine learning concepts. Be prepared for the challenge to be more extensive than initially expected, as it often involves developing a prototype or solving a complex problem.

4. Panel Interviews

The final stage usually consists of a series of panel interviews, which can last several hours. During these interviews, you will meet with various team members, including engineers and product managers. The panel will cover a range of topics, including technical skills, behavioral questions, and discussions about your past experiences. Expect to engage in coding exercises, system design discussions, and questions that assess your ability to work collaboratively within a team.

5. Offer or Feedback

After the panel interviews, candidates can expect a relatively quick turnaround regarding feedback. If successful, you will receive an offer, while those who do not make it through will typically receive constructive feedback on their performance.

As you prepare for your interview, it’s essential to be ready for a variety of questions that reflect both your technical expertise and your ability to fit into New Relic's collaborative culture.

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

Understand the Interview Process

Familiarize yourself with the structure of New Relic's interview process, which typically includes a recruiter phone screen, a technical interview with the hiring manager, a take-home coding challenge, and a panel interview. Knowing what to expect at each stage will help you prepare effectively and reduce anxiety. Be ready to discuss your experience and how it aligns with the role, as well as to demonstrate your technical skills through coding challenges.

Prepare for Technical Challenges

Given the emphasis on machine learning and data science, ensure you are well-versed in relevant techniques and tools. Brush up on your knowledge of Python, SQL, and machine learning libraries such as TensorFlow and PyTorch. Practice coding problems that involve data manipulation, model evaluation, and statistical analysis. Be prepared to explain your thought process during coding challenges, as interviewers appreciate candidates who can articulate their reasoning.

Be Ready for Behavioral Questions

New Relic values cultural fit, so expect behavioral questions that assess how you work in a team and handle challenges. Prepare stories that highlight your problem-solving skills, collaboration, and adaptability. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey the impact of your actions clearly.

Communicate Your Passion for Data Science

Show genuine enthusiasm for the field of data science and the specific work New Relic is doing in AI and observability. Be prepared to discuss recent trends in machine learning and how they relate to the company's goals. This not only demonstrates your knowledge but also your commitment to contributing to New Relic's mission.

Clarify Expectations During the Take-Home Challenge

The take-home coding challenge can be time-consuming, so clarify any ambiguities in the instructions before you begin. If you find the requirements vague, don’t hesitate to reach out for clarification. This shows initiative and a desire to meet expectations accurately. Remember that quality is more important than quantity; focus on delivering a well-structured and efficient solution.

Engage with Your Interviewers

During the panel interview, take the opportunity to engage with your interviewers. Ask insightful questions about the team dynamics, ongoing projects, and the company culture. This not only helps you gauge if New Relic is the right fit for you but also demonstrates your interest in the role and the organization.

Reflect on Feedback

If you receive feedback after any stage of the interview process, take it seriously. Reflect on the comments and use them to improve your performance in subsequent interviews. This shows a growth mindset and a willingness to learn, traits that are highly valued at New Relic.

Stay Positive and Professional

Throughout the interview process, maintain a positive and professional demeanor. Even if you encounter challenges or difficult questions, approach them with a calm and constructive attitude. This will leave a lasting impression on your interviewers and reflect well on your character.

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

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at New Relic. The interview process will assess your technical skills, problem-solving abilities, and cultural fit within the team. Be prepared to discuss your experience with machine learning, data analysis, and your approach to real-world problems.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial. Be clear about the definitions and provide examples of each type.

How to Answer

Discuss the key differences, such as the presence of labeled data in supervised learning versus the absence in unsupervised learning. Provide examples of algorithms used in each category.

Example

“Supervised learning involves training a model on a labeled dataset, where the outcome is known, such as using regression for predicting house prices. In contrast, unsupervised learning deals with unlabeled data, like clustering customers based on purchasing behavior without predefined categories.”

2. Describe a machine learning project you worked on from start to finish.

This question assesses your practical experience and ability to manage a project lifecycle.

How to Answer

Outline the problem, your approach, the techniques used, and the results. Highlight your role and contributions.

Example

“I worked on a project to predict customer churn for a subscription service. I started by gathering and cleaning the data, then used logistic regression to model the likelihood of churn. After validating the model, I presented the findings to stakeholders, which led to targeted retention strategies that reduced churn by 15%.”

3. How do you handle overfitting in your models?

This question tests your understanding of model evaluation and improvement techniques.

How to Answer

Discuss methods such as cross-validation, regularization, and pruning. Explain how you would apply these techniques in practice.

Example

“To combat overfitting, I use cross-validation to ensure my model generalizes well to unseen data. Additionally, I apply regularization techniques like L1 and L2 to penalize overly complex models, which helps maintain a balance between bias and variance.”

4. What is feature engineering, and why is it important?

Feature engineering is a critical aspect of building effective models.

How to Answer

Explain the process of selecting, modifying, or creating features to improve model performance. Discuss its impact on the model's predictive power.

Example

“Feature engineering involves transforming raw data into meaningful features that enhance model performance. For instance, creating interaction terms or aggregating data can reveal hidden patterns that improve the model’s accuracy significantly.”

Statistics & Probability

1. Explain the concept of p-value in hypothesis testing.

This question evaluates your statistical knowledge and ability to interpret results.

How to Answer

Define p-value and its significance in hypothesis testing. Discuss its implications for decision-making.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value (typically < 0.05) suggests that we can reject the null hypothesis, indicating that our findings are statistically significant.”

2. How would you design an A/B test?

A/B testing is a common method for evaluating changes in products or features.

How to Answer

Outline the steps for designing an A/B test, including defining objectives, selecting metrics, and ensuring randomization.

Example

“To design an A/B test, I would first define the objective, such as increasing user engagement. Next, I would randomly assign users to control and treatment groups, ensuring that the sample size is adequate for statistical significance. Finally, I would analyze the results using appropriate metrics to determine the impact of the change.”

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

Understanding the Central Limit Theorem is fundamental for statistical inference.

How to Answer

Explain the theorem and 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 crucial for making inferences about population parameters based on sample statistics.”

Data Analysis

1. Describe your experience with SQL and how you use it in data analysis.

SQL is a vital skill for data scientists, and this question assesses your proficiency.

How to Answer

Discuss your experience with SQL queries, data manipulation, and how you use SQL to extract insights from databases.

Example

“I have extensive experience using SQL for data extraction and manipulation. I often write complex queries involving joins and subqueries to analyze large datasets, which helps me uncover trends and inform decision-making.”

2. How do you approach data cleaning and preprocessing?

Data cleaning is a critical step in the data analysis process.

How to Answer

Outline your methodology for identifying and addressing data quality issues.

Example

“My approach to data cleaning involves several steps: first, I assess the dataset for missing values and outliers. I then apply techniques such as imputation for missing data and normalization for scaling features. This ensures that the data is ready for analysis and modeling.”

3. Can you give an example of a time you uncovered a significant insight from data?

This question evaluates your analytical skills and ability to derive actionable insights.

How to Answer

Share a specific example, detailing the context, analysis performed, and the impact of your findings.

Example

“While analyzing user behavior data, I discovered that a significant portion of users dropped off during the checkout process. By conducting further analysis, I identified a confusing UI element as the culprit. After collaborating with the design team to simplify the checkout flow, we saw a 20% increase in completed transactions.”

Technical Skills

1. What programming languages are you proficient in, and how do you use them in your work?

This question assesses your technical skills and familiarity with relevant tools.

How to Answer

List the programming languages you are proficient in and provide examples of how you have used them in data science projects.

Example

“I am proficient in Python and R, which I use for data analysis and machine learning. For instance, I utilize Python libraries like Pandas and Scikit-learn for data manipulation and model building, while R is my go-to for statistical analysis and visualization.”

2. How do you stay updated with the latest trends in data science and machine learning?

This question evaluates your commitment to continuous learning in a rapidly evolving field.

How to Answer

Discuss the resources you use to stay informed, such as online courses, conferences, or research papers.

Example

“I regularly follow industry blogs, attend webinars, and participate in online courses to stay updated on the latest trends in data science. I also engage with the data science community on platforms like GitHub and LinkedIn to share knowledge and learn from others.”

3. Describe your experience with deploying machine learning models.

This question assesses your practical experience in taking models from development to production.

How to Answer

Outline your experience with deployment processes, tools, and any challenges you faced.

Example

“I have experience deploying machine learning models using AWS Sagemaker. I developed a model for predicting customer churn and successfully deployed it as a REST API. This allowed the marketing team to access real-time predictions, which significantly improved their targeting strategies.”