Fidelity Machine Learning Engineer Interview Questions + Guide in 2024

Fidelity Investments is a privately held financial services company with a mission to strengthen its clients’ financial well-being. The company offers investment and technology solutions to individual investors, institutions, and non-profit organizations to help them deliver employee benefits and manage their clients’ funds.

The Machine Learning Engineer position at Fidelity is integral to the AI Delivery Chapter team. The position focuses on delivering customer-facing AI models into production, leveraging extensive expertise in AWS infrastructure, SQL, Java/Python microservices, and machine learning model deployment. As part of an agile team, you’ll have the opportunity to innovate and implement high-value AI solutions to enhance the lives of Fidelity’s customers.

In this guide, we’ll walk you through the company’s interview process, the commonly asked Fidelity machine learning engineer interview questions, as well as some expert tips to help you prepare better.

Recruiter/Hiring Manager Call Screening

Should your CV make the initial cut, a recruiter from Fidelity’s Talent Acquisition Team will reach out to validate key details concerning your experiences and skill levels. Similarly, expect to answer some behavioral questions.

Occasionally, the hiring manager for the Machine Learning Engineer role may join this initial screening to address your queries about the role and the company itself. Minor technical and behavioral discussions might also occur during this phase.

The screening call generally lasts about 30 minutes.

Technical Virtual Interview

Clearing the recruiter screening earns you an invitation for a technical virtual interview. This is typically conducted through video conferencing and involves screen sharing. During this hour-long stage, you can anticipate questions surrounding Fidelity’s data systems, algorithms, and computational techniques.

For machine learning roles specifically, expect to face coding challenges, solution approaches for problem statements, and discussions about machine learning fundamentals. Demonstrating a solid grasp of Python, Java, or other specified languages, along with familiarity with data structures, and system designs, is crucial.

Onsite Interview Rounds

If successfully navigated, you’ll be invited to Fidelity’s onsite interview loop. This will involve multiple rounds of interviews assessing both your technical prowess and behavioral fit. Some rounds might focus on project-specific challenges, coding problems, or case studies designed to evaluate your problem-solving capabilities in real-world scenarios.

For roles like Machine Learning Engineer, showcasing your ability to implement and optimize models, understanding of MLOps, and mastery of relevant tools (like AWS Sagemaker) can set you apart.

Typically, interviews at Fidelity vary by role and team, but commonly machine learning engineer interviews follow a fairly standardized process across these question topics.

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.

What is the average salary for a Machine Learning Engineer at Fidelity Investments?

What is the company culture like at Fidelity Investments?

Fidelity Investments fosters a collaborative and inclusive environment. The company values honesty, integrity, and continuous learning. Employees are encouraged to innovate, work on impactful projects, and have a healthy work-life balance.

What makes Fidelity Investments a great place for Machine Learning Engineers?

Fidelity offers opportunities to work on cutting-edge AI and ML projects that have a meaningful impact on customers. The company supports professional growth through continuous learning, mentorship, and flexible working arrangements.

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!