Fidelity Investments Machine Learning Engineer Interview Questions + Guide in 2025

Fidelity Investments is a leading financial services company that aims to strengthen the financial well-being of its clients through investment and technology solutions.

The role of a Machine Learning Engineer at Fidelity involves developing and deploying AI models that enhance customer experiences and optimize internal processes. Key responsibilities include designing data pipelines, working with cloud infrastructure (particularly AWS), and implementing machine learning models that automate and personalize services for Fidelity's clients. A strong foundation in Python and SQL is essential, as is experience with machine learning frameworks and tools. Additionally, familiarity with CI/CD practices, microservices architecture, and a proactive, self-driven attitude are traits that will help you thrive in this role. Fidelity's commitment to innovation and customer-centric solutions aligns with the responsibilities of a Machine Learning Engineer, making this position crucial in driving the company's mission forward.

This guide serves to equip you with the essential knowledge and insights needed to excel in your interview for the Machine Learning Engineer position at Fidelity Investments. By understanding the role and the company's values, you'll be better prepared to demonstrate your fit and capabilities effectively.

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Fidelity Investments. The interview process will likely assess your technical skills in machine learning, programming, and data handling, as well as your ability to work in a team and adapt to the company's culture.

Technical Skills

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

Understanding the fundamental concepts of machine learning is crucial for this role, as it will help you articulate your knowledge of model selection and application.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight scenarios where one might be preferred 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 in real-world applications.

How to Answer

Outline the project scope, your role, the challenges encountered, and how you overcame them. Emphasize the impact of your work.

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 for oversampling the minority class, which improved our model's accuracy significantly.”

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

Feature engineering is a critical aspect of building effective machine learning models, and understanding it is essential for this role.

How to Answer

Define feature engineering and discuss its importance in improving model performance. Provide examples of techniques you have used.

Example

“Feature engineering is the process of selecting, modifying, or creating new features from raw data to improve model performance. For instance, in a sales prediction model, I created features like 'days since last purchase' to capture customer behavior better.”

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

This question tests your understanding of model evaluation metrics and their implications.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

“I evaluate model performance using metrics like accuracy for balanced datasets, while precision and recall are crucial for imbalanced datasets. For instance, in a fraud detection model, I prioritize recall to ensure we catch as many fraudulent cases as possible.”

5. Can you explain the concept of overfitting and how to prevent it?

Overfitting is a common issue in machine learning, and understanding it is vital for model robustness.

How to Answer

Define overfitting and discuss techniques to prevent it, such as cross-validation, regularization, and pruning.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern, leading to poor generalization. To prevent it, I use techniques like cross-validation to ensure the model performs well on unseen data and apply regularization methods to penalize overly complex models.”

Programming and Data Handling

1. What are the key differences between Python lists and tuples?

This question assesses your foundational programming knowledge, particularly in Python.

How to Answer

Discuss the characteristics of lists and tuples, including mutability and performance considerations.

Example

“Python lists are mutable, meaning they can be changed after creation, while tuples are immutable. This makes tuples faster and more memory-efficient, which is beneficial when working with large datasets that don’t require modification.”

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

Handling missing data is a critical skill for any data engineer or machine learning engineer.

How to Answer

Discuss various strategies for dealing with missing data, such as imputation, removal, 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 or median imputation for numerical data or drop rows with excessive missing values to maintain data integrity.”

3. Can you explain SQL joins and when to use each type?

SQL skills are essential for data manipulation, and understanding joins is fundamental.

How to Answer

Define the different types of joins (INNER, LEFT, RIGHT, FULL) and provide examples of when to use each.

Example

“INNER JOIN returns records with matching values in both tables, while LEFT JOIN returns all records from the left table and matched records from the right. I use INNER JOIN when I need only the intersecting data, and LEFT JOIN when I want to retain all records from the primary table, even if there are no matches.”

4. Describe a time when you optimized a SQL query. What was the outcome?

This question evaluates your practical experience with SQL performance tuning.

How to Answer

Share a specific example of a query you optimized, the techniques you used, and the results achieved.

Example

“I optimized a slow-running query by adding appropriate indexes and rewriting subqueries as joins. This reduced the execution time from several minutes to under 10 seconds, significantly improving the performance of our reporting dashboard.”

5. What is your experience with cloud platforms, particularly AWS?

Given the role's emphasis on cloud technologies, this question assesses your familiarity with AWS services.

How to Answer

Discuss your experience with AWS services relevant to machine learning, such as S3, EC2, and SageMaker.

Example

“I have extensive experience using AWS, particularly with S3 for data storage and SageMaker for building and deploying machine learning models. I utilized SageMaker’s built-in algorithms to streamline the model training process, which improved our deployment speed.”

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

Understand the Interview Structure

Fidelity Investments typically conducts a multi-round interview process for Machine Learning Engineer roles. Expect an initial phone screen with HR, followed by a technical interview focusing on Python and SQL, and concluding with a panel interview that assesses your machine learning knowledge, situational awareness, and behavioral fit. Familiarize yourself with this structure to prepare effectively for each stage.

Master the Technical Skills

Given the emphasis on Python and SQL in the interviews, ensure you are well-versed in data structures, object-oriented programming, and basic coding mechanisms in Python. For SQL, practice writing complex queries, including window functions and joins. Additionally, brush up on machine learning concepts, model development, and deployment, particularly in cloud environments like AWS, as this knowledge will be crucial during the technical rounds.

Showcase Your Experience with AWS

Fidelity values hands-on experience with AWS infrastructure, so be prepared to discuss your familiarity with AWS services, particularly Sagemaker, and how you have utilized them in past projects. Highlight any experience you have with CI/CD tools, version control, and orchestration tools, as these are relevant to the role and will demonstrate your capability to work in a modern development environment.

Prepare for Behavioral Questions

Behavioral questions are a significant part of the interview process. Reflect on your past experiences and be ready to discuss how you have handled challenges, worked in teams, and contributed to projects. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey your thought process and the impact of your actions.

Emphasize Your Problem-Solving Skills

Fidelity is looking for self-driven and motivated individuals who can tackle complex problems. Be prepared to discuss specific instances where you identified a problem, developed a solution, and implemented it successfully. This will not only showcase your technical skills but also your ability to think critically and work independently.

Align with Company Culture

Fidelity Investments prides itself on a culture of inclusion and professional development. Familiarize yourself with their values and mission to strengthen the financial well-being of clients. During the interview, express your alignment with these values and how you can contribute to the company’s goals. Demonstrating a genuine interest in the company’s mission will resonate well with your interviewers.

Follow Up Professionally

After your interviews, consider sending a follow-up email to express your gratitude for the opportunity and reiterate your interest in the role. While some candidates have reported challenges in receiving feedback, a polite follow-up can help keep you on their radar and demonstrate your professionalism.

By preparing thoroughly and aligning your skills and experiences with Fidelity's expectations, you can position yourself as a strong candidate for the Machine Learning Engineer role. Good luck!

The interview process for a Machine Learning Engineer at Fidelity Investments is structured to assess both technical expertise and cultural fit within the organization. It typically consists of three main rounds, each designed to evaluate different aspects of your qualifications and experience.

1. Initial Phone Screen

The first step in the interview process is a phone screen with a recruiter. This conversation usually lasts about 30 minutes and focuses on your background, relevant experience, and understanding of the role. The recruiter will ask questions to gauge your technical skills, particularly in Python and SQL, as well as your familiarity with machine learning concepts. This is also an opportunity for you to learn more about Fidelity's work culture and the specifics of the Machine Learning Engineer position.

2. Technical Interview

Following the initial screen, candidates typically participate in a technical interview. This round is more in-depth and may involve a combination of coding exercises and theoretical questions. Expect to demonstrate your proficiency in Python and SQL, including data structures, querying, and basic coding mechanisms. Additionally, you may be asked to solve problems related to machine learning modeling and discuss your experience with data transformation pipelines and cloud technologies, particularly AWS.

3. Panel Interview

The final round is a panel interview, which includes multiple interviewers from the team. This session will cover a range of topics, including advanced machine learning concepts, situational awareness, and behavioral questions. The panel will assess your ability to work collaboratively, your problem-solving skills, and how you approach real-world scenarios in machine learning. This round is crucial for determining how well you align with the team’s goals and Fidelity's values.

As you prepare for these interviews, it's essential to be ready for a mix of technical and behavioral questions that reflect the skills and experiences outlined in the job description. Now, let's delve into the specific interview questions that candidates have encountered during this process.

1. Create a function can_shift to determine if string A can be shifted to become string B.

Given two strings A and B, write a function can_shift to return whether or not A can be shifted some number of places to get B.

2. Develop a function str_map to determine if a one-to-one correspondence exists between characters of two strings at the same positions.

Given two strings, string1, and string2, write a function str_map to determine if there exists a one-to-one correspondence (bijection) between the characters of string1 and string2.

3, Write a function to determine if a string is a palindrome.

Given a string, write a function to determine if it is a palindrome or not. A palindrome reads the same forwards and backwards.

4. Create a function to find the index where the sum of the left half equals the right half in a list.

Given a list of integers, find the index at which the sum of the left half of the list is equal to the right half. If no such index exists, return -1.

5. How does random forest generate the forest and why use it over logistic regression?

Explain how random forest generates multiple decision trees and aggregates their results. Discuss the advantages of using random forest over logistic regression, such as handling non-linear data and reducing overfitting.

6. When would you use a bagging algorithm versus a boosting algorithm?

Compare the scenarios where bagging and boosting algorithms are appropriate. Provide examples of the tradeoffs, such as bagging, reducing variance, and boosting and improving accuracy but being more prone to overfitting.

7. How would you evaluate and compare two credit risk models for personal loans?

• Identify the type of model developed by the co-worker for loan approval.
• Describe how to measure the difference between two credit risk models over a timeframe, considering monthly installment payments.
• List the metrics to track for measuring the success of the new model, such as accuracy, precision, recall, and AUC-ROC.

8. What’s the difference between Lasso and Ridge Regression?

Explain the key differences between Lasso and Ridge Regression, focusing on their regularization techniques and how they handle feature selection and multicollinearity.

9. What are the key differences between classification models and regression models?

Describe the fundamental differences between classification and regression models, including their objectives, output types, and common use cases.

10. What are the Z and t-tests, and when should you use each?

Explain the purpose and differences between Z and t-tests, and specify scenarios for their appropriate use.

11. How would you reformat student test score data for better analysis?

Given two datasets of student test scores, identify drawbacks in their current organization, suggest formatting changes, and describe common issues in “messy” datasets.

12. What metrics would you use to determine the value of each marketing channel?

For a company selling B2B analytics dashboards, identify key metrics to evaluate the effectiveness and value of different marketing channels.

13. How would you determine the next partner card for a company using customer spending data?

Using customer spending data, outline the process to identify the most suitable partner for a new co-branded credit card.

14. How would you investigate if a redesigned email campaign led to an increase in conversion rates?

Analyze the impact of a redesigned new-user email journey on conversion rates, considering other potential influencing factors.

15. How would you design a function to detect anomalies in univariate and bivariate datasets?

If given a univariate dataset, how would you design a function to detect anomalies? What if the data is bivariate?

16. What is the expected churn rate in March for customers who bought subscriptions since January 1st?

You noticed that 10% of customers who bought subscriptions in January 2020 canceled before February 1st. Assuming uniform new customer acquisition and a 20% month-over-month decrease in churn, what is the expected churn rate in March for all customers who bought the product since January 1st?

17. How would you explain a p-value to a non-technical person?

Explain what a p-value is in simple terms to someone who is not technical.

• Be Well-Prepared: Fidelity’s interviews combine technical and behavioral evaluations. Brushing up on core algorithms, programming concepts, and machine learning techniques is crucial. To practice these, get paired with our mock interview platform or try our AI interviewer.

• Know Your Projects: Expect to discuss past projects in depth. Be ready to explain your methodologies, technologies used, and the impact of your work.

• Showcase Problem-Solving Skills: Demonstrate your ability to tackle complex problem statements with innovative solutions. Utilize the STAR method (Situation, Task, Action, Result) to structure your responses to behavioral questions effectively.

Navigating the interview process for the Machine Learning Engineer position at Fidelity Investments requires thorough preparation and a strategic approach. With rounds covering technical abilities, behavioral skills, and practical problem-solving, you must be well-prepared to showcase both your technical prowess and interpersonal capabilities.

If you want more insights about the company, check out our main Fidelity Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Fidelity’s interview process for different positions.

Good luck with your interview!