Fidelity Investments Data Scientist Interview Questions + Guide in 2025

Fidelity Investments is a leading financial services firm dedicated to enhancing the financial well-being of its clients through innovative investment solutions.

As a Data Scientist at Fidelity, you’ll be part of a dynamic team that leverages advanced analytics and machine learning to solve complex financial challenges. Your primary responsibilities will involve developing and implementing predictive models, conducting data analysis, and contributing to quantitative research that informs investment strategies. Ideal candidates will possess strong programming skills in languages such as Python or R, experience with machine learning frameworks, and a solid understanding of statistical methods. Familiarity with financial concepts and the ability to communicate complex quantitative findings to non-technical stakeholders are essential traits. Fidelity values collaboration and integrity, making it important for candidates to demonstrate teamwork and ethical decision-making in their previous experiences.

This guide will equip you with targeted insights to prepare for your Data Scientist interview at Fidelity, enhancing your ability to articulate your fit for the role and the company.

The interview process for a Data Scientist role at Fidelity Investments is structured and thorough, designed to assess both technical and interpersonal skills. Candidates can expect multiple rounds of interviews that evaluate their expertise in data science, machine learning, and their ability to collaborate effectively within a team.

1. Initial Screening

The process typically begins with an initial screening, which may be conducted via a phone call with a recruiter or hiring manager. This conversation focuses on understanding the candidate’s background, experience, and motivation for applying to Fidelity. Candidates should be prepared to discuss their resume in detail and articulate their interest in the role and the company.

2. Technical Assessment

Following the initial screening, candidates usually undergo a technical assessment. This may include a coding challenge or a take-home assignment that tests their proficiency in programming languages such as Python or R, as well as their understanding of machine learning algorithms and data manipulation techniques. Candidates might also face questions related to statistics, data structures, and algorithm design during this phase.

3. Behavioral Interviews

Candidates can expect to participate in one or more behavioral interviews. These interviews are often conducted by team members and focus on assessing the candidate’s soft skills, such as teamwork, communication, and problem-solving abilities. Interviewers may ask about past experiences, how candidates handle challenges, and their approach to collaboration within a team setting.

4. Final Technical Interview

The final technical interview typically involves a deeper dive into the candidate’s technical knowledge and problem-solving skills. This may include live coding exercises, discussions about previous projects, and questions that assess the candidate’s understanding of advanced data science concepts, such as machine learning model evaluation, optimization techniques, and data visualization.

5. HR Interview

The last step in the interview process is usually an HR interview, where candidates discuss their career goals, work preferences, and any logistical details related to the position, such as salary expectations and start dates. This interview also serves as an opportunity for candidates to ask questions about the company culture and benefits.

As you prepare for your interview, it’s essential to familiarize yourself with the types of questions that may be asked during each stage of the process.

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

Understand the Interview Structure

Fidelity’s interview process often includes multiple rounds, typically starting with a behavioral interview followed by technical assessments. Be prepared for a mix of group and one-on-one interviews, as candidates have reported both formats. Familiarize yourself with the structure and be ready to discuss your resume and past experiences in detail, as interviewers will likely focus on your background and how it aligns with the role.

Showcase Your Technical Skills

Given the technical nature of the Data Scientist role, ensure you are well-versed in relevant programming languages (Python, SQL, etc.) and machine learning frameworks (like TensorFlow or PyTorch). Candidates have reported being asked to solve coding problems and explain complex algorithms, so practice coding challenges and be ready to articulate your thought process clearly. Brush up on statistical concepts and be prepared to discuss model evaluation and optimization techniques.

Prepare for Behavioral Questions

Fidelity values teamwork and collaboration, so expect behavioral questions that assess your ability to work with others. Reflect on your past experiences and be ready to share specific examples of how you’ve contributed to team projects, resolved conflicts, or led initiatives. Emphasize your communication skills, as the ability to explain complex concepts to non-technical stakeholders is crucial.

Research the Company Culture

Fidelity is known for its supportive and respectful work environment. Familiarize yourself with the company’s values and mission, and be prepared to discuss how your personal values align with theirs. Candidates have noted the importance of demonstrating a genuine interest in the company and its goals, so be ready to articulate why you want to work at Fidelity specifically.

Ask Insightful Questions

Engage your interviewers by asking thoughtful questions about the team dynamics, current projects, and the challenges they face. This not only shows your interest in the role but also helps you gauge if the company culture and work environment are a good fit for you. Candidates have found that interviewers appreciate when candidates come prepared with questions that reflect their understanding of the industry and the company’s position within it.

Follow Up Professionally

After your interview, send a thank-you email to express your appreciation for the opportunity to interview. This is a chance to reiterate your interest in the role and briefly mention any key points from the interview that you found particularly engaging. A professional follow-up can leave a positive impression and keep you top of mind as they make their decision.

By following these tips and preparing thoroughly, you can position yourself as a strong candidate for the Data Scientist role at Fidelity Investments. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Fidelity Investments. The interview process will likely assess a combination of technical skills, problem-solving abilities, and behavioral competencies. Candidates should be prepared to discuss their past experiences, technical knowledge, and how they approach data-driven challenges.

Experience and Background

1. Can you describe a project where you applied machine learning techniques?

This question aims to understand your practical experience with machine learning and how you apply theoretical knowledge to real-world problems.

How to Answer

Discuss a specific project, detailing the problem you were solving, the data you used, the algorithms you implemented, and the outcomes of your work.

Example

“In my last project, I developed a predictive model to forecast customer churn for a retail client. I utilized logistic regression and decision trees, analyzing customer behavior data to identify key factors influencing churn. The model improved retention strategies, leading to a 15% reduction in churn rates.”

2. What is your experience with deep learning frameworks?

This question assesses your familiarity with advanced machine learning techniques and tools.

How to Answer

Mention specific frameworks you have used, the types of problems you solved with them, and any relevant projects.

Example

“I have extensive experience with TensorFlow and PyTorch. For instance, I built a convolutional neural network using TensorFlow to classify images for a healthcare application, achieving an accuracy of over 90% on the validation set.”

Machine Learning

3. Explain the bias-variance trade-off.

This question tests your understanding of a fundamental concept in machine learning.

How to Answer

Define bias and variance, explain their relationship, and discuss how they affect model performance.

Example

“The bias-variance trade-off refers to the balance between a model’s ability to minimize bias, which leads to underfitting, and variance, which can cause overfitting. A good model should have low bias and low variance, but often improving one increases the other. Techniques like cross-validation can help find the right balance.”

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

This question evaluates your knowledge of model assessment techniques.

How to Answer

Discuss various metrics you use for evaluation, depending on the type of problem (classification, regression, etc.).

Example

“I typically use accuracy, precision, recall, and F1-score for classification problems, while for regression, I prefer metrics like RMSE and R-squared. I also emphasize the importance of cross-validation to ensure the model’s robustness.”

Statistics and Probability

5. What is the Central Limit Theorem (CLT) and why is it important?

This question assesses your understanding of statistical principles.

How to Answer

Explain the CLT and its implications for statistical inference.

Example

“The Central Limit Theorem states that the distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the population’s distribution. This is crucial for hypothesis testing and confidence interval estimation, as it allows us to make inferences about population parameters.”

6. Can you explain the concept of p-values?

This question tests your grasp of hypothesis testing.

How to Answer

Define p-values and their role in statistical tests.

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, providing evidence for the alternative hypothesis.”

Data Manipulation and Programming

7. Describe your experience with SQL and how you use it in data analysis.

This question evaluates your data manipulation skills.

How to Answer

Discuss specific SQL queries you have written and the types of data analysis you performed.

Example

“I frequently use SQL to extract and manipulate data from relational databases. For example, I wrote complex queries involving joins and subqueries to analyze customer purchase patterns, which helped inform marketing strategies.”

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

This question assesses your data cleaning and preprocessing skills.

How to Answer

Explain various strategies for dealing with missing data and when to use each.

Example

“I handle missing data by first assessing the extent and pattern of the missingness. Depending on the situation, I might use imputation techniques, such as mean or median substitution, or I may choose to remove records with missing values if they are not significant to the analysis.”

Behavioral Questions

9. Share an experience where you had to work collaboratively in a team.

This question evaluates your teamwork and communication skills.

How to Answer

Describe a specific instance, focusing on your role, the team’s dynamics, and the outcome.

Example

“I worked on a cross-functional team to develop a data-driven marketing strategy. I collaborated with data engineers and marketing specialists, ensuring that our insights were actionable. Our combined efforts led to a 20% increase in campaign effectiveness.”

10. How do you prioritize tasks when working on multiple projects?

This question assesses your time management and organizational skills.

How to Answer

Discuss your approach to prioritization and any tools or methods you use.

Example

“I prioritize tasks based on deadlines and the impact of each project. I use project management tools like Trello to keep track of progress and ensure that I allocate time effectively to meet all deadlines without compromising quality.”

• Create a function can_shift to determine if string A can be shifted to get 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.

• 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.

• Write a function to determine if a string is a palindrome. Create a function to check if a string is a palindrome — a word that reads the same forwards and backward (e.g., ‘reviver,’ ‘madam,’ ‘deified,’ ‘civic’).

• Create a function to find the index where the sum of the left half equals the right half. 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 there is no index where this condition is satisfied, return -1.

• How does random forest generate the forest and why use it over logistic regression? Explain how random forest generates multiple decision trees and combines their results. Discuss the advantages of using random forest over logistic regression, such as handling non-linear data and reducing overfitting.

• When would you use a bagging algorithm versus a boosting algorithm? Compare the use cases for bagging and boosting algorithms. Provide examples of tradeoffs, such as bagging reducing variance and boosting improving accuracy but being more prone to overfitting.

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

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

• 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.

• What are the key differences between classification models and regression models? Describe the main differences between classification and regression models, including their objectives, output types, and common use cases.

• What are the Z and t-tests, and when should you use each? Explain the purpose and differences between Z and t-tests. Describe scenarios where one test is preferred over the other.

• What are the drawbacks of the given student test score data layouts, and how would you reformat them? Analyze the provided student test score datasets for potential issues. Suggest formatting changes to make the data more useful for analysis. Discuss common problems in “messy” datasets.

• What metrics would you use to determine the value of each marketing channel? Given data on marketing channels and their costs, identify key metrics to evaluate the value of each channel.

• How would you determine the next partner card using customer spending data? Using customer spending data, outline a method to identify the best potential partner for a new credit card offering.

• How would you investigate if the redesigned email campaign led to the increase in conversion rates? Given the fluctuating conversion rates, design an approach to determine if the new email campaign caused the recent increase in new-user to customer conversion rates, considering other potential factors.

• 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?

• What are the drawbacks of the given student test score datasets, and how would you reformat them? Assume you have data on student test scores in two layouts (dataset 1 and dataset 2). What are the drawbacks of these formats? What changes would you make for better analysis? Describe common problems in “messy” datasets.

• 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 since January 1st?

• 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.

• What are Z and t-tests, and when should you use each? Describe Z and t-tests, their uses, differences, and when to use one over the other.

What is the average salary for a Data Scientist at Fidelity Investments?

Q: What is the interview process for the Data Scientist position at Fidelity Investments? The interview process at Fidelity Investments typically involves an initial telephonic call with the hiring manager followed by technical and HR interviews. The technical interview will focus on your resume, past projects, and key technical skills like machine learning and probability. The HR interview includes quintessential questions like “Tell me about yourself,” your hobbies, and other get-to-know-you questions.

Q: What are some common technical questions asked during the Data Scientist interview at Fidelity Investments? You may be asked to explain concepts such as LSTM in one sentence, AUC curves, and basic statistics like sampling. Interviewers might also ask about your favorite R packages or to explain regression and residual plots through your past projects.

Q: What skills and qualifications are required for the Data Scientist position at Fidelity Investments? Candidates should ideally have a Master’s degree in relevant fields like Engineering, Computer Science, or Mathematics, and experience with supervised and unsupervised machine learning techniques. Proficiency in Python, SQL, and experience with tools like TensorFlow, PyTorch, or Spark will be crucial.

Q: What is the work environment like at Fidelity Investments for a Data Scientist? Fidelity promotes a collaborative and friendly environment with a focus on both personal and professional growth. The company encourages continuous learning and innovation, making it an exciting place to advance your career.

Q: How can I prepare for my interview at Fidelity Investments? Research the company and its data science applications, review your technical skills, and practice common interview questions. Use Interview Query to practice and brush up on the types of questions you could be asked during the technical rounds.

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.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Fidelity Investments data scientist interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!