Freddie Mac is a leading player in the housing finance industry, dedicated to making homeownership and rental housing more accessible and affordable across the United States. With a commitment to innovation, Freddie Mac continuously seeks to implement data-driven solutions to optimize business operations.

As a Data Analyst at Freddie Mac, you will be part of the Single-Family Data and Decisions team. This role involves working with various teams to understand technological needs, applying architectural principles, and providing technical mentorship. You will support platform teams in prioritizing work and collaborating with data teams to deliver business capabilities. The position requires a blend of strong technical skills, including data analysis, Python proficiency, and experience with cloud technologies, along with excellent communication and problem-solving abilities. Join Freddie Mac to contribute to building a more efficient housing finance system and making a significant impact on people’s lives.

Submitting Your Application

The first step in the interview process for a Data Analyst position at Freddie Mac is to submit a compelling application that reflects your technical skills and interest in joining the company. Whether you were contacted by a Freddie Mac 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 Freddie Mac 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 Freddie Mac data analyst 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 data analyst role at Freddie Mac usually is conducted through virtual means, such as video conferences and screen sharing. Questions in this stage, which lasts around 1 hour, may revolve around Freddie Mac’s data systems, ETL pipelines, SQL queries, and basic data analytical questions.

In addition to technical questions, there will be a Python coding exercise that might be done live, requiring you to share your screen. Take-home assignments regarding product metrics, analytics, and data visualization may also be incorporated.

Onsite Interview Rounds

Following a successful technical screening, you’ll be invited to attend the onsite interview loop. This involves multiple interview rounds with various team members and managers. The focus will be on evaluating your technical prowess, including programming and data analysis capabilities, as well as behavioral aspects.

The onsite rounds will likely include further technical questions, deeper dives into data-related issues, and scenario-based questions relevant to the role. Expect conversations about your experience with different data tools and platforms, such as Hadoop, AWS, Tableau, and data governance practices.

Quick Tips For Freddie Mac Data Analyst Interviews

To help you excel in your interview at Freddie Mac, here are a few tips derived from previous interview experiences:

• Review Your Resume Thoroughly: Interviewers often revolve around your past experiences listed in your CV. Make sure you are ready to discuss your previous projects, roles, and any specific challenges you faced.

• Brush Up on Technical Skills: Be prepared for coding exercises, particularly in SQL and Python. Familiarize yourself with data visualization tools and methods, as well as procedures for handling structured and unstructured data.

• Understand Freddie Mac’s Business and Vision: Interviewers may ask questions to gauge your understanding of Freddie Mac’s mission and impact. Being well-versed with their initiatives in housing finance, data strategies, and recent developments can make a considerable difference.

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

• Write a Python program to check if each string in a list has all the same characters. Given a list of strings, write a Python program to check whether each string has all the same characters or not. Determine the complexity of this program.

• 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.

• Create a function to simulate coin tosses based on a given probability of heads. Write a function that takes the number of tosses and a probability of heads, returning a list of randomly generated results for the coin tosses.

• Develop a function to perform bootstrap sampling and calculate a confidence interval. Given an array of numerical values, bootstrap samples, and a size for a confidence interval, write a function to perform bootstrap sampling and calculate the confidence interval.

• Write a program to determine the term frequency (TF) values for each term in a document. Given a text document in the form of a string, write a program in Python to determine the term frequency (TF) values for each term in the document. Round the term frequency to 2 decimal points.

• How would you explain what a p-value is to someone who is not technical? Explain a p-value as a measure of how likely it is that an observed result occurred by chance. A lower p-value indicates that the result is less likely to be due to random chance.

• Write a function to simulate coin tosses with a given probability of heads. Create a function that takes the number of tosses and the probability of heads as inputs. The function should return a list of 'H' for heads and 'T' for tails, based on the given probability.

• How much do you expect to pay for a sports game ticket, considering a 20% chance of a scalped ticket not working? Calculate the expected cost by considering the probability of the scalped ticket working and the additional cost if it doesn't. Determine how much money to set aside for the game based on this calculation.

• What is the probability of drawing three cards in increasing order from a shuffled deck of 500 cards? Calculate the probability that each subsequent card drawn from a shuffled deck of 500 cards is larger than the previous one.

• How do you calculate the average lifetime value for a SAAS company with given churn and subscription costs? Determine the formula for average lifetime value using the product cost, monthly churn rate, and average customer duration. Calculate the average lifetime value based on these parameters.

• How would you evaluate whether using a decision tree algorithm is the correct model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan. How would you evaluate if a decision tree is the right model for this problem?

• How would you evaluate the performance of a decision tree model before and after deployment? If you decide to use a decision tree model, how would you assess its performance before and after deployment?

• What is the concept of LDA in machine learning and its use cases? Explain the concept of Linear Discriminant Analysis (LDA) in machine learning. What are some practical use cases for LDA?

• How would you collect and aggregate unstructured video data for an ETL pipeline? You are designing an ETL pipeline for a model that uses videos as input. How would you collect and aggregate unstructured data from videos?

• How would you create a system to detect firearm listings on a marketplace? You are designing a marketplace where selling firearms is prohibited. How would you create a system to automatically detect if a listing is selling a gun?

• How would you design a YouTube video recommendation algorithm? You are tasked with building the YouTube video recommendation algorithm. How would you design the recommendation system, and what important factors should you consider?

• What is the formula for calculating the average lifetime value for a SAAS company? You work for a SAAS company with a product costing $100/month, a 10% monthly churn rate, and an average customer lifespan of 3.5 months. Calculate the formula for the average lifetime value.

• What metrics/graphs/models would you use to analyze churn behavior for different pricing plans? Netflix has two pricing plans: $15/month or $100/year. An executive wants an analysis of churn behavior for these plans. What metrics, graphs, or models would you build to provide an overarching view of subscription performance?

• How would you analyze cross-platform user experience for web and mobile? You need to understand user behavior, preferences, and engagement patterns by analyzing user interaction data on both web and mobile. Write a query to determine the percentage of users who visited only mobile, only web, and both.

• How would you select and test a pre-launch of a new show on Amazon Prime Video? Amazon Prime Video wants to pre-launch a new show to 10,000 customers. How would you select these customers and measure the show's performance during the pre-launch?

• What would you expect after applying a new UI that increased conversion rates by 5% in a test? A new UI tested on a random subset of users increased the target metric by 5%. What would you expect to happen to the metric after applying the new UI to all users, assuming no novelty effect?

Looking to kickstart or advance your career as a Data Analyst? Freddie Mac offers an inspiring, inclusive environment where you can make a meaningful impact on the housing finance system. The interview process at Freddie Mac, while comprehensive, is approachable and emphasizes both technical skills and cultural fit. With multiple rounds focusing on behavioral and technical questions, you'll have the opportunity to showcase your problem-solving abilities and your knowledge of data analytics, Python, and more.

Excited about the opportunity but uncertain about the interview process? Dive into our in-depth Freddie Mac Interview Guide, where you'll find a wealth of insights and sample questions to help you prepare. Whether you’re aiming for a data analyst or another role, Interview Query equips you with the knowledge, confidence, and strategic guidance you need to excel.

Explore our company interview guides for detailed information on Freddie Mac and many other top firms. If you have any questions, feel free to reach out. Good luck with your interview preparation, and aim high with Freddie Mac!