Angi Data Scientist Interview Questions + Guide in 2025

Angi is a leading online marketplace dedicated to connecting homeowners with skilled local professionals for a wide range of home service projects, ensuring that every job is done well.

As a Data Scientist at Angi, you will play a pivotal role in enhancing the company's mission by leveraging advanced machine learning and statistical analysis to drive significant business outcomes. Your primary responsibilities will include the development and deployment of sophisticated machine learning models aimed at improving user experience, optimizing the service provider's lifecycle, and detecting fraudulent activities within the platform. You will be expected to collaborate closely with cross-functional teams, including engineers and product managers, to integrate data-driven solutions into Angi's services seamlessly.

Successful candidates will possess a strong quantitative background, with a Master's or Ph.D. in fields such as Computer Science, Statistics, or Mathematics, and have 5 to 7+ years of experience in data science and machine learning, especially in marketplace environments. Moreover, expertise in SQL, Python, and cloud ML solutions is essential, along with a proven ability to communicate complex technical concepts to non-technical stakeholders effectively. A passion for fostering innovation and mentoring junior team members will further solidify your fit for this role.

This guide aims to equip you with the insights and knowledge necessary for excelling in your interview with Angi, ensuring you stand out as a candidate who not only meets technical qualifications but also aligns with the company's values and vision.

The interview process for a Data Scientist role at Angi is structured to assess both technical and interpersonal skills, ensuring candidates align with the company's mission and culture. The process typically consists of several key stages:

1. Initial Phone Screen

The first step is a phone interview with a recruiter, which usually lasts about 30-45 minutes. During this conversation, the recruiter will discuss the role, the company culture, and your background. They will assess your fit for the position and gauge your interest in Angi. Expect questions about your experience, skills, and motivations for applying.

2. Technical Interview

Following the initial screen, candidates typically undergo a technical interview, which may be conducted via video call. This interview focuses on your data science expertise, including your proficiency in SQL, Python, and machine learning concepts. You may be asked to solve problems or discuss past projects that demonstrate your technical capabilities. Be prepared to explain your thought process and approach to problem-solving.

3. Onsite Interviews

The onsite interview process usually involves multiple rounds with various team members, including data scientists, engineers, and product managers. Each interview lasts approximately 45 minutes to an hour. These sessions will cover a mix of technical and behavioral questions. You may be asked to present a case study or a project you have worked on, showcasing your model development and deployment experience. Additionally, expect discussions around collaboration with cross-functional teams and your approach to innovation and mentorship.

4. Final Interview

The final stage often includes a conversation with senior leadership or directors. This interview focuses on your long-term vision, alignment with Angi's goals, and your ability to contribute to the company's mission. They will assess your communication skills, particularly your ability to convey complex technical concepts to non-technical stakeholders.

Throughout the process, candidates should demonstrate their passion for data science, their ability to work collaboratively, and their commitment to continuous learning and innovation.

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

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

Understand the Company’s Mission and Values

Angi is dedicated to providing exceptional home service experiences, and understanding this mission will help you align your responses with the company's goals. Familiarize yourself with their services, recent initiatives, and how data science plays a role in enhancing user experience and trust on the platform. This knowledge will not only demonstrate your interest in the company but also allow you to articulate how your skills can contribute to their mission.

Prepare for a Non-Technical Interview Environment

Based on previous interview experiences, it appears that Angi's interview process may focus more on management and product selection rather than deep technical questions. Be prepared to discuss your experience in a way that highlights your strategic thinking and problem-solving abilities. Frame your technical expertise in the context of how it can drive business outcomes, particularly in fraud detection and user experience improvement.

Showcase Collaboration Skills

Collaboration is key at Angi, as the role involves working closely with cross-functional teams. Be ready to share examples of how you have successfully collaborated with engineers, product managers, and other stakeholders in past projects. Highlight your ability to communicate complex technical concepts to non-technical audiences, as this will be crucial in ensuring that your data-driven solutions are effectively integrated into the company’s products and services.

Emphasize Innovation and Continuous Learning

Angi values innovation and a culture of continuous learning. Prepare to discuss how you have fostered innovation in your previous roles, whether through exploring new techniques, mentoring junior team members, or implementing best practices in machine learning. Share specific examples of how you have contributed to a culture of learning and knowledge sharing, as this aligns with the expectations for the role.

Be Ready to Discuss Model Deployment and Impact

Given the emphasis on deploying machine learning models at scale, be prepared to discuss your experience with MLOps practices, including model training, versioning, and monitoring. Highlight any specific projects where your models had a measurable impact on business metrics, particularly in fraud detection or user engagement. This will demonstrate your ability to not only develop models but also ensure their successful implementation in a production environment.

Personalize Your Questions

At the end of the interview, you will likely have the opportunity to ask questions. Use this time to inquire about the team dynamics, ongoing projects, and how success is measured in the role. Tailor your questions to reflect your understanding of Angi’s mission and the specific challenges they face in the marketplace. This will show your genuine interest in the position and help you assess if the company is the right fit for you.

By following these tips, you will be well-prepared to showcase your skills and fit for the Data Scientist role at Angi. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Angi. The interview process will likely focus on your technical expertise in machine learning, data strategy, model deployment, and your ability to collaborate with cross-functional teams. Be prepared to discuss your experience with fraud detection, model evaluation, and your approach to innovation and mentorship.

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 characteristics of both supervised and unsupervised learning, emphasizing how supervised learning uses labeled data while unsupervised learning identifies patterns in unlabeled data.

Example

“Supervised learning involves training a model on a labeled dataset, where the outcome is known, such as predicting house prices based on features like size and location. 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 led. What were the challenges and outcomes?

This question assesses your practical experience and problem-solving skills in real-world applications.

How to Answer

Outline the project scope, your role, the challenges faced, and the results achieved. Highlight any innovative solutions you implemented.

Example

“I led a project to develop a fraud detection model for an e-commerce platform. The main challenge was dealing with imbalanced data. I implemented SMOTE to balance the dataset and used ensemble methods, which improved our detection rate by 30% while reducing false positives.”

3. What techniques do you use for feature selection?

Feature selection is critical for model performance, and your approach can reveal your depth of knowledge.

How to Answer

Discuss various techniques such as recursive feature elimination, LASSO regression, or tree-based methods, and explain when you would use each.

Example

“I often use recursive feature elimination for its effectiveness in reducing overfitting. For instance, in a recent project, I applied it to a customer segmentation model, which helped improve the model's accuracy by focusing on the most relevant features.”

4. How do you handle overfitting in your models?

Overfitting is a common issue in machine learning, and your strategies for addressing it are important.

How to Answer

Mention techniques like cross-validation, regularization, and pruning, and provide examples of how you’ve applied them.

Example

“To combat overfitting, I typically use cross-validation to ensure my model generalizes well to unseen data. In a recent classification task, I applied L2 regularization, which helped reduce the model complexity and improved its performance on the validation set.”

5. Can you explain how you would evaluate the performance of a machine learning model?

Evaluation metrics are essential for understanding model effectiveness, especially in a business context.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain their relevance based on the problem context.

Example

“I evaluate model performance using precision and recall, especially in fraud detection, where false negatives can be costly. For instance, I developed a model that achieved a precision of 85% and a recall of 90%, which significantly reduced fraudulent transactions.”

Statistics & Probability

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

This question tests your understanding of statistical principles that underpin many machine learning algorithms.

How to Answer

Explain the theorem and its implications for sampling distributions and inferential statistics.

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 hypothesis testing and confidence interval estimation in data analysis.”

2. How do you approach hypothesis testing?

Your approach to hypothesis testing can reveal your analytical thinking and understanding of statistical significance.

How to Answer

Outline the steps of hypothesis testing, including formulating null and alternative hypotheses, selecting a significance level, and interpreting results.

Example

“I start by defining my null and alternative hypotheses based on the research question. After selecting a significance level, I perform the appropriate test, such as a t-test, and interpret the p-value to determine if I can reject the null hypothesis.”

3. Explain the concept of p-value. What does it signify?

Understanding p-values is fundamental in statistics, and your explanation should reflect clarity and depth.

How to Answer

Define p-value and discuss its role in hypothesis testing, including common misconceptions.

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 suggests that the observed data is unlikely under the null hypothesis, leading us to consider rejecting it.”

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

This question assesses your understanding of the risks associated with hypothesis testing.

How to Answer

Clearly define both types of errors and provide examples to illustrate their implications.

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 trial, a Type I error could mean falsely concluding a drug is effective, while a Type II error could mean missing a truly effective drug.”

5. How do you ensure the validity of your statistical models?

This question evaluates your approach to maintaining the integrity of your analyses.

How to Answer

Discuss methods such as cross-validation, checking assumptions, and using robust statistical techniques.

Example

“I ensure model validity by performing cross-validation to assess generalizability and checking assumptions like normality and homoscedasticity. Additionally, I use robust statistical methods when assumptions are violated to maintain the integrity of my results.”