Fast Data Scientist Interview Questions + Guide in 2025

Fast is a company focused on revolutionizing the e-commerce landscape through innovative technology and streamlined processes.

As a Data Scientist at Fast, you will be responsible for analyzing complex datasets to derive actionable insights that inform business strategies and enhance user experiences. Your key responsibilities will include developing predictive models, performing statistical analyses, and collaborating with cross-functional teams to implement data-driven solutions. A strong background in machine learning, statistical modeling, and programming languages such as Python or R will be essential. You should also possess excellent problem-solving skills and the ability to communicate complex findings to non-technical stakeholders effectively.

Successful candidates will demonstrate a passion for leveraging data to drive business outcomes and a commitment to the company's mission of enhancing the online shopping experience. Given the company's emphasis on teamwork and innovation, adaptability and a collaborative mindset will also be crucial traits for thriving in this role.

This guide will aid you in preparing for a job interview by providing insights into what to expect and key areas to focus on during your preparation.

The interview process for a Data Scientist role at Fast is structured and designed to assess both technical skills and cultural fit. It typically consists of several stages, each focusing on different aspects of the candidate's qualifications and personality.

1. Initial Phone Interview

The process begins with a 25-30 minute phone interview, usually conducted by a recruiter. This initial conversation is primarily focused on getting to know the candidate, discussing their background, and understanding their motivations for applying to Fast. Expect a mix of behavioral questions and a few basic technical inquiries, such as concepts related to object-oriented programming and SQL. Candidates should be prepared to articulate their strengths, weaknesses, and interest in the company.

2. Technical Assessment

Following the initial interview, candidates may be required to complete a technical assessment. This can take the form of a logic quiz or a coding challenge, often conducted online. The assessment typically includes pseudocode problems and logic puzzles that test problem-solving abilities. Candidates should be ready to demonstrate their coding skills and explain their thought processes as they work through the problems.

3. Technical Interview

Successful candidates will then move on to a technical interview, which may be conducted via video conferencing. This interview usually lasts about an hour and involves more in-depth technical questions. Interviewers may ask candidates to solve coding problems in real-time, discuss their previous projects, and explain key concepts in data science and programming. Candidates should be prepared to write code on the spot and discuss their approach to problem-solving.

4. Behavioral and Cultural Fit Interview

In addition to technical skills, Fast places a strong emphasis on cultural fit. Candidates will likely participate in a behavioral interview, where they will be asked questions that assess their interpersonal skills, teamwork, and adaptability. This interview may involve scenarios that require candidates to demonstrate how they would handle specific situations, particularly in a client-facing context.

5. Final Interview

The final stage of the interview process may involve a more comprehensive interview with multiple team members, including HR and technical staff. This round often includes a mix of technical and behavioral questions, as well as discussions about the candidate's willingness to relocate and work in various environments. Candidates should be ready to ask insightful questions about the company and the role to demonstrate their interest and engagement.

As you prepare for your interview, consider the types of questions that may arise in each of these stages.

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

Embrace the Company Culture

Fast is known for its friendly and respectful interview process. Approach your interview with a positive attitude and be prepared to engage in a genuine conversation. Show enthusiasm for the company and its values, and be ready to articulate why you want to work there. Reflect on how your personal values align with the company’s mission, and be prepared to discuss this during your interview.

Prepare for a Mix of Technical and Behavioral Questions

Expect a blend of technical and behavioral questions during your interview. While technical skills are essential, the interviewers also want to gauge your problem-solving approach and interpersonal skills. Be ready to explain your thought process clearly when tackling coding or logic problems. Practice articulating your reasoning as you work through problems, as this will demonstrate your analytical skills and ability to communicate effectively.

Brush Up on Core Concepts

Familiarize yourself with fundamental programming concepts, particularly in Java and SQL, as these are commonly referenced in interviews. Be prepared to answer questions about object-oriented programming, inheritance, and the differences between various programming constructs. Additionally, practice writing pseudocode and solving basic coding problems, as these are often part of the interview process.

Be Ready for Logic and Problem-Solving Questions

Fast places a significant emphasis on logic and problem-solving abilities. You may encounter questions that require you to think critically and solve puzzles. Practice logic puzzles and familiarize yourself with common problem-solving techniques. This will not only help you perform well in the interview but also demonstrate your ability to think on your feet.

Show Willingness to Relocate

Given that the role may involve relocation, be prepared to discuss your flexibility and willingness to move. Highlight any previous experiences that demonstrate your adaptability and openness to new environments. This will reassure the interviewers that you are committed to the role and the company’s needs.

Ask Thoughtful Questions

At the end of your interview, take the opportunity to ask insightful questions. This shows your interest in the role and the company. Inquire about team dynamics, project goals, or the company’s future direction. Thoughtful questions can leave a lasting impression and demonstrate your genuine interest in contributing to the team.

Follow Up with Gratitude

After your interview, send a thank-you email to express your appreciation for the opportunity to interview. This not only reinforces your interest in the position but also reflects your professionalism. A well-crafted follow-up can set you apart from other candidates and keep you top of mind for the hiring team.

By following these tips, you can approach your interview with confidence and a clear strategy, increasing your chances of success at Fast. Good luck!

Experience and Background

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Fast. The interview process will likely assess your technical skills, problem-solving abilities, and cultural fit within the company. Be prepared to discuss your experience, demonstrate your knowledge of data science concepts, and showcase your ability to communicate complex ideas clearly.

Machine Learning

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

Understanding the fundamental types of machine learning is crucial for a Data Scientist role.

How to Answer

Clearly define both supervised and unsupervised learning, providing examples of each. Highlight the scenarios in which you would use one over the other.

Example

“Supervised learning involves training a model on labeled data, 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, aiming to find hidden patterns, like clustering customers based on purchasing behavior.”

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

Discuss a specific project, the methodologies you used, and the challenges you encountered. Emphasize how you overcame these challenges.

Example

“I worked on a project to predict customer churn for a subscription service. One challenge was dealing with imbalanced data. I implemented techniques like SMOTE to generate synthetic samples and improved the model's performance significantly.”

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

This question tests your understanding of model evaluation metrics.

How to Answer

Mention various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. Explain when to use each metric based on the context of the problem.

Example

“I evaluate model performance using accuracy for balanced datasets, but for imbalanced datasets, I prefer precision and recall. For instance, in a fraud detection model, I focus on recall to ensure we catch as many fraudulent cases as possible.”

4. What techniques do you use for feature selection?

This question gauges your knowledge of improving model performance through feature engineering.

How to Answer

Discuss various techniques like recursive feature elimination, LASSO regression, and tree-based methods. Explain why feature selection is important.

Example

“I use recursive feature elimination to systematically remove features and assess model performance. This helps in reducing overfitting and improving model interpretability.”

Statistics & Probability

1. Explain the Central Limit Theorem and its significance.

This question tests your foundational knowledge in statistics.

How to Answer

Define the Central Limit Theorem and explain its importance in 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 significant because it allows us to make inferences about population parameters using sample statistics.”

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

Understanding hypothesis testing is crucial for data analysis.

How to Answer

Define both types of errors and provide examples to illustrate the differences.

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 when it is not.”

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

This question assesses your data cleaning and 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 imputation for small amounts of missing data or consider more advanced techniques like KNN imputation for larger datasets.”

4. Can you explain the concept of p-value?

This question tests your understanding of statistical significance.

How to Answer

Define p-value and explain its role in hypothesis testing.

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 we can reject the null hypothesis, indicating statistical significance.”

Programming & Technical Skills

1. Write a function to calculate the factorial of a number.

This question assesses your coding skills and understanding of recursion.

How to Answer

Explain your thought process while writing the function, focusing on clarity and efficiency.

Example

“I would write a recursive function that multiplies the number by the factorial of the number minus one until it reaches one. This approach is straightforward and leverages the definition of factorial.”

2. What is the difference between a list and a tuple in Python?

This question tests your knowledge of data structures in Python.

How to Answer

Clearly outline the differences in mutability, syntax, and use cases.

Example

“A list is mutable, meaning it can be changed after creation, while a tuple is immutable. Lists are defined with square brackets, while tuples use parentheses. I typically use tuples for fixed collections of items and lists for collections that may change.”

3. How would you optimize a slow-running SQL query?

This question evaluates your database management skills.

How to Answer

Discuss various optimization techniques such as indexing, query restructuring, and analyzing execution plans.

Example

“I would start by analyzing the execution plan to identify bottlenecks. Adding appropriate indexes can significantly speed up query performance, and restructuring the query to minimize joins or using subqueries can also help.”

4. Explain the concept of object-oriented programming (OOP).

This question tests your understanding of programming paradigms.

How to Answer

Define OOP and its core principles, such as encapsulation, inheritance, and polymorphism.

Example

“OOP is a programming paradigm based on the concept of ‘objects,’ which can contain data and methods. Key principles include encapsulation, which restricts access to certain components, inheritance, allowing new classes to inherit properties from existing ones, and polymorphism, enabling methods to do different things based on the object it is acting upon.”