Factset Machine Learning Engineer Interview Questions + Guide in 2025

FactSet is a leading provider of financial data and analytics, empowering investment professionals with the tools and insights needed to make informed decisions.

As a Machine Learning Engineer at FactSet, you will play a crucial role in developing and implementing machine learning models that enhance data analysis and financial forecasting. Your key responsibilities will include designing algorithms, processing large datasets, and optimizing models for performance and scalability. You'll collaborate with data scientists and software engineers to integrate machine learning solutions into existing systems, ensuring that the technology aligns with FactSet's high standards for accuracy and reliability.

To excel in this position, you should possess strong programming skills in languages such as Python, C++, or Java, with a solid understanding of machine learning frameworks and libraries. Familiarity with data structures, algorithms, and software development principles is essential, as you'll need to effectively tackle coding challenges and optimize performance. Additionally, an analytical mindset and the ability to communicate complex concepts clearly will set you apart.

This guide will provide you with the insights and knowledge necessary to prepare effectively for your interview, helping you demonstrate your technical prowess and alignment with FactSet's mission and culture.

The interview process for a Machine Learning Engineer at FactSet is structured and involves multiple stages designed to assess both technical skills and cultural fit.

1. Initial HR Screening

The process typically begins with an initial phone screening conducted by an HR representative. This conversation lasts around 30-45 minutes and focuses on your background, interest in the role, and general fit for the company culture. Expect questions about your resume, your motivations for applying, and your availability.

2. Online Assessment

Following the HR screening, candidates usually complete an online assessment, often hosted on platforms like HackerRank. This assessment includes coding challenges that test your knowledge of data structures, algorithms, and programming languages relevant to the role, such as Python or C++. The assessment typically consists of multiple questions, ranging from easy to medium difficulty, and is designed to evaluate your problem-solving skills and coding proficiency.

3. Technical Interviews

Candidates who perform well in the online assessment are invited to participate in one or more technical interviews. These interviews can be conducted live, either via video call or in-person, and typically last between 45 minutes to an hour. During these sessions, you will be asked to solve coding problems in real-time, discuss your approach to algorithms, and demonstrate your understanding of machine learning concepts. Interviewers may also delve into your previous projects and experiences, assessing your ability to apply theoretical knowledge to practical scenarios.

4. Final Interview Round

The final round often includes a combination of technical and HR interviews. You may meet with senior engineers or managers who will evaluate your technical skills further, as well as your fit within the team. This round may involve system design questions, discussions about your past work, and behavioral questions to gauge your teamwork and communication abilities.

5. Offer and Feedback

After the final interviews, candidates can expect to receive feedback within a few days to a week. If selected, an offer will be extended, and discussions regarding salary and start dates will take place.

As you prepare for your interview, it's essential to be ready for a variety of questions that will test both your technical expertise and your understanding of machine learning principles.

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

Understand the Technical Landscape

As a Machine Learning Engineer, you will be expected to have a solid grasp of various programming languages and frameworks. Brush up on Python, C++, and SQL, as these are frequently mentioned in interviews. Familiarize yourself with machine learning libraries such as TensorFlow and PyTorch, and be prepared to discuss your experience with them. Additionally, understanding data structures and algorithms is crucial, as many coding questions will focus on these areas.

Prepare for Coding Assessments

Expect to face multiple coding assessments, often through platforms like HackerRank. These assessments typically include questions on arrays, strings, linked lists, and various searching and sorting algorithms. Practice solving problems of varying difficulty levels, as interviewers may present a mix of easy, medium, and hard questions. Focus on optimizing your solutions and articulating your thought process clearly, as interviewers appreciate candidates who can explain their reasoning.

Showcase Your Projects and Experience

Be ready to discuss your previous projects in detail, especially those that relate to machine learning and data analysis. Highlight your role, the challenges you faced, and the impact of your work. Interviewers are interested in your hands-on experience, so be prepared to dive deep into the technical aspects of your projects, including the algorithms you used and the results you achieved.

Emphasize Problem-Solving Skills

During technical interviews, you may encounter questions that require you to demonstrate your problem-solving abilities. Practice breaking down complex problems into manageable parts and explaining your approach step-by-step. Interviewers often look for candidates who can think critically and adapt their strategies as new information arises.

Engage with the Interviewers

The interview process at FactSet is known to be friendly and conversational. Take the opportunity to engage with your interviewers by asking insightful questions about the team, projects, and company culture. This not only shows your interest in the role but also helps you assess if FactSet is the right fit for you.

Be Prepared for Behavioral Questions

In addition to technical assessments, expect behavioral questions that assess your teamwork, communication skills, and adaptability. Prepare examples from your past experiences that demonstrate your ability to work collaboratively, handle challenges, and learn from failures. Highlighting your enthusiasm for the company and its values can also leave a positive impression.

Stay Calm and Confident

Interviews can be nerve-wracking, but maintaining a calm and confident demeanor is essential. Practice mock interviews to build your confidence and improve your communication skills. Remember that the interviewers are not just evaluating your technical skills but also your fit within the team and company culture.

By following these tips and preparing thoroughly, you can position yourself as a strong candidate for the Machine Learning Engineer role at FactSet. Good luck!

Coding and Algorithms

1. Can you explain the difference between depth-first search (DFS) and breadth-first search (BFS)?

Understanding the fundamental algorithms is crucial for a Machine Learning Engineer, as they often relate to data structure traversal and optimization.

How to Answer

Discuss the core principles of both algorithms, including their use cases and performance implications. Highlight scenarios where one might be preferred over the other.

Example

“Depth-first search explores as far as possible along each branch before backtracking, making it useful for scenarios like maze solving. In contrast, breadth-first search explores all neighbors at the present depth prior to moving on to nodes at the next depth level, which is ideal for finding the shortest path in unweighted graphs.”

2. How would you approach solving a problem with a linked list?

Linked lists are a common data structure, and being able to manipulate them is essential for many coding challenges.

How to Answer

Explain your thought process for traversing, inserting, and deleting nodes in a linked list. Provide a brief example of a common operation.

Example

“To reverse a linked list, I would use two pointers: one for the current node and another for the previous node. I would iterate through the list, adjusting the pointers until I reach the end, effectively reversing the links between nodes.”

3. What is time complexity, and why is it important?

Time complexity is a critical concept in algorithm design, and understanding it is vital for optimizing code.

How to Answer

Define time complexity and discuss its significance in evaluating the efficiency of algorithms, especially in the context of large datasets.

Example

“Time complexity measures the amount of time an algorithm takes to complete as a function of the length of the input. It’s important because it helps predict performance and scalability, allowing us to choose the most efficient algorithm for a given problem.”

4. Can you describe a situation where you optimized an algorithm?

Optimization is key in machine learning and software engineering, and interviewers want to see your practical experience.

How to Answer

Share a specific example where you identified a performance bottleneck and the steps you took to improve it.

Example

“I worked on a sorting algorithm that initially had a time complexity of O(n^2). By implementing quicksort, I reduced the time complexity to O(n log n), which significantly improved the performance for large datasets.”

5. How do you handle edge cases in your code?

Edge cases can often lead to unexpected behavior, so it’s important to demonstrate your awareness of them.

How to Answer

Discuss your approach to identifying and testing edge cases during development and testing phases.

Example

“I always consider edge cases during the design phase. For instance, when working with arrays, I ensure to handle cases like empty arrays or arrays with a single element. I also write unit tests specifically targeting these scenarios to ensure robustness.”

Machine Learning Concepts

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

Understanding the types of machine learning is fundamental for a Machine Learning Engineer.

How to Answer

Define both terms and provide examples of algorithms or applications for each.

Example

“Supervised learning involves training a model on labeled data, such as using regression for predicting house prices. Unsupervised learning, on the other hand, deals with unlabeled data, like clustering customers based on purchasing behavior.”

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

Overfitting is a common issue in machine learning, and interviewers want to know your strategies for addressing it.

How to Answer

Discuss the concept of overfitting and various techniques to mitigate it, such as regularization or cross-validation.

Example

“Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern. To prevent it, I use techniques like L1/L2 regularization, dropout in neural networks, and cross-validation to ensure the model generalizes well to unseen data.”

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

Evaluating model performance is crucial, and knowing the right metrics is key.

How to Answer

List relevant metrics and explain when to use each, such as accuracy, precision, recall, and F1 score.

Example

“I would use accuracy for balanced datasets, but for imbalanced datasets, precision and recall are more informative. The F1 score is useful when we need a balance between precision and recall, especially in cases like fraud detection.”

4. Describe a machine learning project you have worked on.

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

How to Answer

Provide a brief overview of the project, your role, the challenges faced, and the outcomes.

Example

“I developed a predictive model for customer churn using logistic regression. I collected and preprocessed the data, selected features, and tuned hyperparameters. The model achieved an accuracy of 85%, which helped the marketing team target at-risk customers effectively.”

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

Handling missing data is a common challenge in data preprocessing.

How to Answer

Discuss various strategies for dealing with missing data, such as imputation or removal.

Example

“I typically analyze the extent of missing data first. If it’s minimal, I might remove those records. For larger gaps, I use imputation techniques, like filling in the mean or median for numerical data, or using predictive models to estimate missing values.”

SQL and Data Management

1. Can you explain the difference between INNER JOIN and LEFT JOIN?

SQL knowledge is essential for data manipulation and retrieval.

How to Answer

Define both types of joins and provide examples of when to use each.

Example

“INNER JOIN returns only the rows with matching values in both tables, while LEFT JOIN returns all rows from the left table and matched rows from the right table. For instance, if I want all customers regardless of whether they have placed an order, I would use a LEFT JOIN.”

2. How would you optimize a slow SQL query?

Performance optimization is crucial in data management.

How to Answer

Discuss techniques such as indexing, query restructuring, or analyzing execution plans.

Example

“To optimize a slow SQL query, I would first analyze the execution plan to identify bottlenecks. Then, I might add indexes on frequently queried columns or rewrite the query to reduce complexity, ensuring it runs more efficiently.”

3. What is normalization, and why is it important?

Normalization is a key concept in database design.

How to Answer

Define normalization and discuss its benefits in reducing redundancy and improving data integrity.

Example

“Normalization is the process of organizing data in a database to minimize redundancy. It’s important because it helps maintain data integrity and makes it easier to manage and update the database without inconsistencies.”

4. Can you write a SQL query to find the second highest salary from a table?

This is a common SQL interview question that tests your query-writing skills.

How to Answer

Provide a clear and concise SQL query that accomplishes the task.

Example

“SELECT MAX(salary) FROM employees WHERE salary < (SELECT MAX(salary) FROM employees);”

5. How do you ensure data quality in your datasets?

Data quality is critical for accurate analysis and modeling.

How to Answer

Discuss methods for validating and cleaning data, such as data profiling and consistency checks.

Example

“I ensure data quality by performing data profiling to identify anomalies and inconsistencies. I also implement validation rules during data entry and regularly audit datasets to maintain accuracy and reliability.”