Factset Data Scientist Interview Questions + Guide in 2025

FactSet provides integrated financial information and analytical applications to the global investment community, enabling clients to make informed investment decisions.

As a Data Scientist at FactSet, you will play a crucial role in analyzing complex datasets to derive actionable insights that drive business value. Key responsibilities include developing and implementing predictive models, conducting statistical analyses, and utilizing machine learning techniques to enhance data-driven decision-making processes. You will collaborate closely with cross-functional teams to understand their data needs and provide solutions that align with FactSet’s commitment to delivering high-quality financial analytics.

The ideal candidate for this position will have a strong foundation in programming languages such as Python and SQL, as well as a solid understanding of data structures, algorithms, and statistical methods. Experience with machine learning frameworks, data visualization tools, and a passion for solving complex problems will set you apart. In addition to technical skills, exceptional communication abilities and a collaborative mindset are essential, as you will need to effectively convey complex concepts to non-technical stakeholders.

This guide will help you prepare for the interview by familiarizing you with the expectations and skills needed, allowing you to present yourself confidently and effectively.

The interview process for a Data Scientist role at FactSet is structured and thorough, designed to assess both technical skills and cultural fit. It typically unfolds over several stages, each focusing on different competencies relevant to the position.

1. Initial HR Screening

The process begins with an initial phone screening conducted by an HR representative. This conversation usually lasts around 30-45 minutes and aims to gauge your interest in the role, discuss your background, and clarify any logistical details such as availability and salary expectations. The HR representative may also ask about your understanding of FactSet and why you are interested in joining the company.

2. Online Assessment

Following the HR screening, candidates are often required to complete an online assessment, typically hosted on platforms like HackerRank. This assessment includes coding questions that test your knowledge of data structures, algorithms, and programming languages relevant to the role. Expect to encounter a mix of easy to medium-level problems, including array manipulations, string operations, and basic algorithm challenges.

3. Technical Interviews

Candidates who perform well in the online assessment will move on to one or more technical interviews. These interviews are usually conducted by senior engineers or team members and can take place in a live coding format. You will be asked to solve coding problems in real-time, demonstrating your problem-solving skills and coding proficiency. Questions may cover a range of topics, including object-oriented programming, SQL queries, and data manipulation techniques. Be prepared to explain your thought process and optimize your solutions.

4. Behavioral Interview

In addition to technical skills, FactSet places a strong emphasis on cultural fit and teamwork. A behavioral interview is typically conducted to assess your interpersonal skills, adaptability, and alignment with the company's values. Expect questions that explore your past experiences, challenges you've faced, and how you work within a team. This round may also include discussions about your resume and any relevant projects.

5. Final Interview

The final stage often involves a more in-depth discussion with a senior manager or director. This interview may cover both technical and behavioral aspects, allowing you to showcase your expertise and discuss your career aspirations. You might be asked to elaborate on your previous work, explain complex concepts, or even design a solution to a hypothetical problem relevant to the role.

As you prepare for your interview, keep in mind 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 Structure

The interview process at FactSet typically consists of multiple rounds, including technical assessments and HR interviews. Familiarize yourself with the structure: an initial coding assessment, followed by technical interviews focusing on data structures, algorithms, and possibly system design. Knowing what to expect will help you manage your time and energy effectively throughout the process.

Master Core Technical Skills

As a Data Scientist, you will likely face questions on data structures, algorithms, and programming languages such as Python and SQL. Brush up on your knowledge of arrays, linked lists, sorting algorithms, and time complexity. Practice coding problems on platforms like HackerRank to get comfortable with the format and types of questions you may encounter. Be prepared to explain your thought process and the reasoning behind your solutions.

Showcase Your Projects and Experience

During the interviews, you will have the opportunity to discuss your previous work and projects. Be ready to articulate your contributions, the challenges you faced, and the outcomes. Highlight any relevant experience in data analysis, machine learning, or statistical modeling. This not only demonstrates your technical skills but also your ability to apply them in real-world scenarios.

Emphasize Problem-Solving and Communication Skills

FactSet values candidates who can think critically and communicate effectively. Be prepared to discuss how you approach problem-solving, including any frameworks or methodologies you use. Additionally, practice articulating your thoughts clearly and concisely, as you may need to explain complex concepts to non-technical stakeholders.

Prepare for Behavioral Questions

Expect to encounter behavioral questions that assess your fit within the company culture. Reflect on your past experiences and be ready to share examples that demonstrate your teamwork, adaptability, and leadership skills. Consider using the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you provide a comprehensive view of your experiences.

Show Enthusiasm for FactSet

Demonstrating genuine interest in FactSet and its mission can set you apart from other candidates. Research the company’s products, values, and recent developments. Be prepared to discuss why you want to work there and how your goals align with the company’s objectives. This will not only show your enthusiasm but also your commitment to contributing to the team.

Be Ready for a Collaborative Environment

Interviews at FactSet often involve multiple interviewers, and the atmosphere is generally friendly and collaborative. Approach the interviews as a conversation rather than an interrogation. Engage with your interviewers, ask questions, and be open to feedback during coding challenges. This will help you build rapport and demonstrate your ability to work well in a team-oriented environment.

Follow Up Thoughtfully

After your interviews, consider sending a thank-you email to express your appreciation for the opportunity to interview. Use this as a chance to reiterate your interest in the role and the company. A thoughtful follow-up can leave a positive impression and keep you top of mind as they make their decision.

By preparing thoroughly and approaching the interview with confidence and enthusiasm, you can position yourself as a strong candidate for the Data Scientist role at FactSet. Good luck!

Coding and Algorithms

1. Can you explain the difference between a stack and a queue?

Understanding data structures is crucial for a data scientist role, as they are foundational to algorithm design and implementation.

How to Answer

Discuss the key characteristics of both data structures, including their use cases and how they handle data.

Example

“A stack is a Last In First Out (LIFO) structure, where the last element added is the first to be removed. In contrast, a queue operates on a First In First Out (FIFO) basis, where the first element added is the first to be removed. Stacks are often used in scenarios like function call management, while queues are useful in scheduling tasks.”

2. Describe how you would implement a binary search algorithm.

This question tests your understanding of searching algorithms and their efficiency.

How to Answer

Explain the steps of the binary search algorithm and its time complexity.

Example

“To implement a binary search, I would first sort the array. Then, I would repeatedly divide the search interval in half, comparing the target value to the middle element. If the target is less than the middle element, I would search the left half; if greater, the right half. This algorithm has a time complexity of O(log n).”

3. How would you find the first non-repeating character in a string?

This question assesses your problem-solving skills and understanding of string manipulation.

How to Answer

Outline a clear approach to solving the problem, including any data structures you would use.

Example

“I would use a hash map to count the occurrences of each character in the string. Then, I would iterate through the string a second time to find the first character with a count of one. This approach has a time complexity of O(n).”

4. Explain how you would reverse a linked list.

This question evaluates your knowledge of linked lists and pointer manipulation.

How to Answer

Describe the iterative or recursive approach you would take to reverse the linked list.

Example

“To reverse a linked list iteratively, I would maintain three pointers: previous, current, and next. I would iterate through the list, adjusting the pointers so that each node points to the previous node instead of the next. This process continues until I reach the end of the list, at which point the previous pointer will be the new head.”

5. What is the time complexity of your solution for merging two sorted arrays?

This question tests your understanding of algorithm efficiency.

How to Answer

Discuss the merging process and its time complexity.

Example

“Merging two sorted arrays involves iterating through both arrays and comparing their elements. The time complexity of this operation is O(n + m), where n and m are the lengths of the two arrays.”

Machine Learning

1. What is the difference between supervised and unsupervised learning?

This question assesses your foundational knowledge of machine learning concepts.

How to Answer

Explain the key differences and provide examples of each type of learning.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features. Unsupervised learning, on the other hand, deals with unlabeled data, where the model tries to identify patterns or groupings, like clustering customers based on purchasing behavior.”

2. Can you explain what overfitting is and how to prevent it?

This question evaluates your understanding of model performance and generalization.

How to Answer

Define overfitting and discuss techniques to mitigate it.

Example

“Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern. To prevent overfitting, I would use techniques such as cross-validation, regularization, and pruning in decision trees.”

3. Describe a project where you applied machine learning. What challenges did you face?

This question allows you to showcase your practical experience and problem-solving skills.

How to Answer

Discuss the project, your role, and the specific challenges you encountered.

Example

“In a project to predict customer churn, I faced challenges with imbalanced data. I addressed this by using techniques like SMOTE for oversampling the minority class and adjusting the classification threshold to improve model performance.”

4. What metrics would you use to evaluate a classification model?

This question tests your knowledge of model evaluation techniques.

How to Answer

Discuss various metrics and their relevance to different scenarios.

Example

“I would consider metrics such as accuracy, precision, recall, and F1-score. For imbalanced datasets, I would prioritize precision and recall to ensure the model performs well on both classes.”

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

This question assesses your data preprocessing skills.

How to Answer

Explain different strategies for dealing with missing data.

Example

“I would first analyze the extent and pattern of missing data. Depending on the situation, I might choose to impute missing values using mean, median, or mode, or I might remove rows or columns with excessive missing data to maintain the integrity of the dataset.”

Statistics and Probability

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

This question evaluates your understanding of statistical significance.

How to Answer

Define p-value and its role in hypothesis testing.

Example

“The p-value measures the probability of obtaining results at least as extreme as the observed results, assuming the null hypothesis is true. A low p-value indicates strong evidence against the null hypothesis, leading to its rejection.”

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

This question tests your grasp of fundamental statistical concepts.

How to Answer

Discuss the theorem and its implications for statistical inference.

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.”

3. How would you explain the difference between Type I and Type II errors?

This question assesses your understanding of error types in hypothesis testing.

How to Answer

Define both types of errors and 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. Understanding these errors is essential for evaluating the reliability of statistical tests.”

4. Can you describe a situation where you used A/B testing?

This question allows you to demonstrate your practical application of statistical methods.

How to Answer

Discuss the A/B testing process and its outcomes.

Example

“I conducted an A/B test to evaluate two different landing page designs for a marketing campaign. By randomly assigning users to each version and measuring conversion rates, I was able to determine that one design significantly outperformed the other, leading to a 20% increase in conversions.”

5. What is the purpose of regression analysis?

This question tests your knowledge of statistical modeling techniques.

How to Answer

Explain regression analysis and its applications.

Example

“Regression analysis is used to model the relationship between a dependent variable and one or more independent variables. It helps in predicting outcomes and understanding the strength and nature of relationships, which is valuable in various fields, including finance and marketing.”