Fidelity Investments is a leading financial services company known for its diverse investment products and innovative solutions. The firm assists clients in managing their financial well-being, investing for the future, and providing institutional and advisory services.

For those interested in a Data Scientist position, Fidelity offers opportunities to join their dedicated teams working on cutting-edge projects. The role involves developing machine learning models to identify suspicious transactions and customers using AI and ML systems. Responsibilities include data cleaning, model development with algorithms like decision trees and neural networks, and collaborating with team members to deliver solutions.

If you seek to make an impactful contribution to the financial industry, this guide will help you navigate the interview process and better understand what Fidelity expects from a Data Scientist candidate.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Fidelity Investments as a Data Scientist. Whether you were contacted by a Fidelity recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Fidelity Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Fidelity data scientist hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Fidelity data scientist role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Fidelity’s data systems, ETL pipelines, and SQL queries.

In the case of data scientist roles, take-home assignments regarding algorithms, machine learning techniques, and model implementation may be incorporated. Apart from these, your proficiency against hypothesis testing, regression analysis, probability distributions, and machine learning fundamentals may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Fidelity office. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the data scientist role at Fidelity.

Quick Tips For Fidelity Investments Data Scientist Interviews

• Know Your Basics: Brush up on your fundamentals in statistics, machine learning, and algorithms. Be prepared to field questions on these topics and explain concepts clearly.

• Review Your Projects Thoroughly: Given that many Fidelity interviews are project-focused, ensure you can discuss all aspects of your past projects in detail, including methodologies, tools, and outcomes.

• Prepare for Behavioral Questions: While technical proficiency is key, don't overlook preparing for behavioral and situational questions that reflect your interpersonal skills and cultural fit.

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

• 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?

• 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 installments.

• 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!