Top 18 Blackrock Data Analyst Interview Questions + Guide in 2025

BlackRock, a leading global investment management corporation, is renowned for its commitment to helping clients experience financial well-being. Joining BlackRock as a Data Analyst involves a dynamic role within their ETF and Index Investments (EII) business. This position requires a blend of technical acumen, analytical skills, and the ability to collaborate effectively across various teams and regions.

In this guide, hosted by Interview Query, we’ll navigate through the interview process and typical Blackrock data analyst interview questions and provide actionable insights to help you succeed. From technical assessments to behavioral rounds, you’ll be well-prepared to embark on a rewarding career path at BlackRock. Let’s get started!

The interview process usually depends on the role and seniority; however, you can expect the following in a Blackrock data analyst interview:

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the BlackRock 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, such as:

• Tell me about yourself.
• Tell me what you know about the company.
• Why are you interested in our company?

This initial call usually takes about 15 minutes.

Pre-recorded Interview

In this stage, you will respond to pre-recorded questions provided by BlackRock. You will typically be given 30 seconds to think and 90 seconds to answer each question. Some of the questions you might encounter include:

• Tell us about one thing you are passionate about.
• Tell me about a time you were in conflict.
• What’s your greatest strength?

This part of the application process is quite straightforward and usually takes about 5 minutes.

First Round: Zoom Interviews

The first round consists of a Zoom interview with a manager and an analyst. This stage focuses on both technical and resume-based questions. Expect questions like:

• Walk through your resume.
• Summarize the mechanics of a mortgage bond.
• Why did you choose BlackRock?
• What are your day-to-day duties in your current role?

This round assesses your technical know-how and fit within the company’s culture and should last around 45 minutes.

Second Round: VP Interview

The second round involves a more in-depth discussion with a VP. This stage can include a comprehensive evaluation of your technical, behavioral, and financial knowledge. Topics could cover:

• Summarize the causes of the 2008 mortgage meltdown.
• What is the relationship between MBS and ABS?
• What are credit default swaps and total return swaps?
• Tell me about the most impressive collaboration experience you’ve had.

This round takes approximately 45 minutes.

Final Round: Onsite Interviews

The final round is an in-person set of interviews with multiple team members. Each interviewer will usually assess different aspects such as technical skills, cultural fit, and alignment with BlackRock’s values. The process might include:

• Detailed behavioral questions.
• Technical evaluations involving coding or puzzles.
• Discussions about your career aspirations.
• Examine your proficiency in tools like SQL, Python and data visualization platforms like Tableau.

Expect each interview to last around 30 minutes, and prepare for a rigorous but rewarding day.

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

1. Write a function search_list to check if a target value is in a linked list.

Write a function, search_list, that returns a boolean indicating if the target value is in the linked_list or not. You receive the head of the linked list, which is a dictionary with the following keys: value and next. If the linked list is empty, you’ll receive None.

2. Write a query to find users who placed less than 3 orders or ordered less than $500 worth of product.

Write a query to identify the names of users who placed less than 3 orders or ordered less than $500 worth of product.

3. Create a function digit_accumulator to sum every digit in a string representing a floating-point number.

You are given a string that represents some floating-point number. Write a function, digit_accumulator, that returns the sum of every digit in the string.

4. Develop a function to parse the most frequent words used in poems.

A literary newspaper hires you for an unusual project. They want you to use your data science skills to parse the most frequent words used in poems. Poems are given as a list of strings called sentences. Return a dictionary of the frequency of words used in the poem.

5. Write a function rectangle_overlap to determine if two rectangles overlap.

You are given two rectangles, a and b, each defined by four ordered pairs denoting their corners on the x, y plane. Write a function rectangle_overlap to determine whether or not they overlap. Return True if so, and False otherwise.

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

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

7. What are the drawbacks of the given student test score data layouts, and how would you reformat them?

Assume you have data on student test scores in two layouts (dataset 1 and dataset 2). Identify the drawbacks of these layouts, suggest formatting changes for better analysis, and describe common problems in “messy” datasets.

8. What is the expected churn rate in March for customers who bought a subscription 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, calculate the expected churn rate in March for all customers who bought the product since January 1st.

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

Describe a p-value in simple terms for someone who is not technical.

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

Explain Z and t-tests, their uses and differences, and when to use one over the other.

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

Given the marketing costs for different channels at a B2B analytics company, identify the metrics you would use to evaluate the value of each marketing channel.

12. How would you determine the next partner card based on customer spending data?

Using customer spending data, outline the process for identifying the most suitable partner for a new partner card, similar to Starbucks or Whole Foods Chase credit cards.

13. How would you investigate if the redesigned email campaign led to the increase in conversion rates?

Given the fluctuating conversion rates before and after a new email campaign, describe how you would determine if the redesigned email journey caused the increase in conversion rates or if other factors were involved.

14. 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.

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

Compare two machine learning algorithms. Describe scenarios where bagging (e.g., random forest) is preferred for reducing variance and boosting (e.g., AdaBoost) is preferred for reducing bias. Provide examples of tradeoffs between the two.

16. 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 new model’s success, such as accuracy, precision, recall, and AUC-ROC.

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

Explain the differences between Lasso and Ridge Regression, focusing on their regularization techniques. Highlight how Lasso performs feature selection by shrinking coefficients to zero, while Ridge penalizes large coefficients without eliminating features.

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

Describe the fundamental differences between classification and regression models. Classification models predict categorical outcomes, while regression models predict continuous outcomes. Discuss examples and use cases for each type.

To help you succeed in your Blackrock data analyst interviews, consider these tips based on interview experiences:

• Understand Financial Concepts: Brush up on your finance knowledge, as questions often blend finance and data analysis (e.g., different types of bonds, credit swaps).
• Detail-Oriented Responses: BlackRock’s interviewers are highly detail-oriented. Be prepared to provide evidence or stories to support every claim on your resume.
• Practice Makes Perfect: Utilize Interview Query to practice for technical questions and mock interviews, focusing on both your data and finance skills.

What is the average salary for a Data Analyst at Blackrock?

According to Glassdoor, data analysts at Blackrock earn between $125K to $191K per year, with an average of $153K per year.

What kind of technical skills are required for a Data Analyst at BlackRock?

BlackRock looks for candidates with strong analytical skills, proficiency in SQL, and data visualization tools like Tableau. Knowledge of at least one programming language, such as Python, is highly desirable. Your ability to manipulate and analyze complex data from various sources is crucial for the role.

What is the company culture like at BlackRock?

BlackRock fosters an environment of collaboration and continuous learning. Employees are encouraged to innovate and take calculated risks. The company values integrity, teamwork, and professional growth, and it offers a hybrid work model to allow for flexibility and in-person collaboration.

The interview process for the Data Analyst position at BlackRock is detailed and rigorous, involving various stages from initial screenings to multiple technical and behavioral interviews. These stages are designed to thoroughly assess candidates’ technical acumen, problem-solving skills, and cultural fit within the organization.

At Interview Query’s BlackRock Interview Guide, we delve into numerous potential interview questions and offer extensive resources to help you excel in your interview preparation. Additionally, Interview Query provides comprehensive guides for other roles, such as Software Engineer and Data Analyst, offering tailored insights into BlackRock’s interview processes for various positions.

At Interview Query, we aim to equip you with the knowledge, confidence, and strategic guidance necessary to tackle all interview challenges. Unlock your full potential and ensure you’re ready to impress at every stage of the BlackRock interview journey.

Good luck with your interview preparations!