Progressive Leasing Machine Learning Engineer Interview Questions + Guide in 2025

Progressive Leasing is a leading provider of in-store and e-commerce lease-to-own solutions, known for its innovative approach and commitment to simplifying financial transactions for its customers.

As a Machine Learning Engineer at Progressive Leasing, you will play a crucial role in developing and implementing machine learning solutions that drive business outcomes. Your key responsibilities will include designing and developing applications that support the company's analytical platforms, automating processes, and collaborating closely with data scientists and other engineers. You will also be expected to actively participate in code reviews and contribute to best practices in software craftsmanship. A strong emphasis on algorithms and statistical modeling will be central to your work, as you will need to apply these concepts to real-world applications that impact customer decision-making and risk assessment.

The ideal candidate for this role will have a solid technical background with experience in machine learning platform development, proficiency in programming languages like Python, and familiarity with cloud environments such as AWS and Azure. A collaborative mindset and the ability to communicate effectively with both technical teams and business stakeholders are essential, as is a commitment to continuous improvement and innovation.

This guide aims to equip you with insights into the role and expectations at Progressive Leasing, allowing you to better prepare for your interview and showcase your fit for the company’s dynamic environment.

The interview process for a Machine Learning Engineer at Progressive Leasing is structured to assess both technical expertise and cultural fit within the organization. It typically consists of several rounds, each designed to evaluate different aspects of a candidate's qualifications and alignment with the company's values.

1. Initial Screening

The process begins with a 30-minute screening call with a recruiter. This conversation is primarily focused on understanding your background, experience, and motivation for applying to Progressive Leasing. The recruiter may also discuss the company's mission and values, ensuring that candidates have a foundational understanding of the organization and its operations.

2. Technical Interview

Following the initial screening, candidates usually participate in a technical interview. This round often involves two parts: a discussion of your previous machine learning projects and a series of technical questions. Expect to delve into topics such as machine learning workflows, probability, statistics, and data handling techniques, including how to manage missing data. Candidates may also be presented with case studies that require analytical thinking and problem-solving skills relevant to the company's business challenges.

3. Managerial Interview

The next step typically involves an interview with a hiring manager or a senior member of the data science team. This round focuses on assessing your technical skills in greater depth, including your understanding of machine learning algorithms, programming languages like Python, and your experience with data science tools and methodologies. Candidates may be asked to solve real-world problems or case studies that reflect the challenges faced by Progressive Leasing.

4. Onsite or Panel Interview

The final stage of the interview process is often an onsite or panel interview, which may include multiple team members and stakeholders. This round is designed to evaluate both technical and behavioral competencies. Expect a mix of technical questions, scenario-based inquiries, and discussions about your approach to teamwork and collaboration. This is also an opportunity for you to demonstrate your understanding of the company's products and how your skills can contribute to their success.

Throughout the interview process, candidates should be prepared to showcase their problem-solving abilities, adaptability, and technical expertise, particularly in machine learning and data analysis.

As you prepare for your interviews, consider the types of questions that may arise in each round, focusing on your experiences and how they align with the expectations of the role.

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

Prepare for a Technical Deep Dive

Expect the interview to dive straight into technical questions without much introduction. Familiarize yourself with the machine learning project workflow, as this is a common topic. Be ready to discuss your past projects in detail, focusing on the methodologies you used, the challenges you faced, and how you overcame them. This will demonstrate your hands-on experience and problem-solving skills.

Master Probability and Statistics

Given the emphasis on probability and statistics in the interview process, ensure you have a solid grasp of these concepts. Be prepared to answer questions about handling missing data, calculating expected values, and discussing statistical models. Brush up on your knowledge of A/B testing, logistic regression, and other relevant statistical methods, as these are likely to come up during your discussions.

Engage with Case Studies

Case studies are a significant part of the interview process. Practice solving hypothetical business problems that Progressive Leasing might face, particularly those related to credit and risk assessment. Think through the data you would need, how you would analyze it, and the potential outcomes. This will not only showcase your analytical skills but also your understanding of the company's business model.

Know the Company and Its Products

Understanding Progressive Leasing's business model and the industry it operates in is crucial. Familiarize yourself with their lease-to-own solutions and how they differentiate themselves in the FinTech space. If possible, try using their product to gain firsthand experience, which will help you answer questions more effectively and demonstrate your genuine interest in the company.

Emphasize Collaboration and Communication

Progressive Leasing values teamwork and collaboration. Be prepared to discuss how you have worked with cross-functional teams in the past, particularly in a remote or hybrid environment. Highlight your ability to communicate complex technical concepts to non-technical stakeholders, as this will be important in your role as a Machine Learning Engineer.

Be Ready for Behavioral Questions

In addition to technical questions, expect behavioral questions that assess your adaptability, problem-solving skills, and teamwork. Use the STAR (Situation, Task, Action, Result) method to structure your responses, providing clear examples from your past experiences that align with the company’s values.

Stay Positive and Professional

Despite any negative experiences shared by candidates, maintain a positive and professional demeanor throughout the interview process. If faced with unexpected questions or a disorganized interview, stay calm and focused. Your ability to handle pressure and uncertainty can set you apart from other candidates.

Follow Up Thoughtfully

After your interviews, consider sending a thoughtful follow-up email to express your appreciation for the opportunity and reiterate your interest in the role. This can help you stand out and demonstrate your enthusiasm for joining the team.

By preparing thoroughly and approaching the interview with confidence, you can position yourself as a strong candidate for the Machine Learning Engineer role at Progressive Leasing. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Progressive Leasing. The interview process will likely focus on your technical expertise in machine learning, statistics, and algorithms, as well as your ability to apply these skills to real-world business problems. Be prepared to discuss your past projects, demonstrate your problem-solving skills, and showcase your understanding of the company's operations and industry.

Machine Learning

1. Can you describe the machine learning project workflow you typically follow?

Understanding the workflow is crucial for a Machine Learning Engineer, as it demonstrates your ability to manage projects from conception to deployment.

How to Answer

Outline the key stages of the workflow, including data collection, preprocessing, model selection, training, evaluation, and deployment. Emphasize your experience with each stage.

Example

“I typically follow a structured workflow that begins with defining the problem and gathering relevant data. After preprocessing the data to handle missing values and outliers, I select appropriate models based on the problem type. I then train and evaluate the models using cross-validation techniques before deploying the best-performing model into production.”

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

Handling missing data is a common challenge in machine learning, and your approach can significantly impact model performance.

How to Answer

Discuss various strategies for dealing with missing data, such as imputation, deletion, or using algorithms that support missing values. Provide examples of when you’ve applied these techniques.

Example

“I handle missing data by first analyzing the extent and pattern of the missingness. Depending on the situation, I might use imputation techniques like mean or median substitution, or I may choose to delete rows or columns if the missing data is excessive. In some cases, I’ve also used models that can handle missing values directly, which can be particularly useful in large datasets.”

Statistics & Probability

3. Explain the concept of expected value and how it applies to risk assessment in financial models.

Understanding expected value is essential for evaluating potential outcomes in financial contexts.

How to Answer

Define expected value and explain its significance in decision-making, particularly in finance. Use a relevant example to illustrate your point.

Example

“Expected value is a fundamental concept in probability that represents the average outcome of a random variable. In financial models, it helps assess risk by calculating the potential returns of different scenarios. For instance, if a leasing company evaluates a new product, the expected value can guide decisions by weighing the likelihood of defaults against potential profits.”

4. How would you calculate the expected default rate for a leasing product?

This question tests your ability to apply statistical concepts to real-world financial products.

How to Answer

Walk through the steps of calculating the expected default rate, including identifying relevant variables and using historical data to inform your calculations.

Example

“To calculate the expected default rate, I would first gather historical data on customer defaults and payment behaviors. I would then analyze this data to determine the probability of default for different customer segments. By applying these probabilities to the current customer base, I can estimate the expected default rate for the new leasing product.”

Algorithms

5. What is the difference between logistic regression and random forest?

This question assesses your understanding of different algorithms and their applications.

How to Answer

Briefly explain both algorithms, highlighting their strengths and weaknesses, and when you would choose one over the other.

Example

“Logistic regression is a linear model used for binary classification, which is easy to interpret and works well with smaller datasets. In contrast, random forest is an ensemble method that combines multiple decision trees to improve accuracy and reduce overfitting. I would choose logistic regression for simpler problems where interpretability is key, while I would opt for random forest when dealing with complex datasets with non-linear relationships.”

6. Can you describe a machine learning model you developed and the impact it had?

This question allows you to showcase your practical experience and the value you can bring to the company.

How to Answer

Detail the model you developed, the problem it addressed, and the results it achieved. Focus on metrics that demonstrate its effectiveness.

Example

“I developed a predictive model to assess customer credit risk, which utilized logistic regression. By analyzing historical customer data, the model improved our risk assessment accuracy by 20%, leading to a significant reduction in default rates and an increase in overall profitability for the leasing division.”

Business Acumen

7. How would you approach automating manual underwriting processes?

This question evaluates your ability to apply machine learning solutions to business challenges.

How to Answer

Discuss the steps you would take to analyze the current underwriting process, identify areas for automation, and implement machine learning solutions.

Example

“I would start by mapping out the current manual underwriting process to identify bottlenecks and inefficiencies. Next, I would gather relevant data to train a machine learning model that predicts creditworthiness based on historical outcomes. By automating this process, we could significantly reduce processing time and improve decision accuracy.”

8. What data would you consider essential for evaluating a customer’s eligibility for leasing?

This question tests your understanding of the data landscape in the leasing industry.

How to Answer

Identify key data points that are relevant for assessing customer eligibility, and explain why they are important.

Example

“Essential data points for evaluating a customer’s eligibility would include credit score, income level, employment history, and previous leasing behavior. These factors provide a comprehensive view of the customer’s financial stability and likelihood of fulfilling lease obligations.”