Progressive Leasing Data Scientist candidates report early calls mixing profit problems and ML fundamentals. One described a 30-minute business case call where knowing the product and lease financing mattered as much as technical skill.
$123K
Avg. Base Comp
$135K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Two candidates for the Data Scientist role describe an early call that leans on business reasoning as much as modeling. One was invited by recruiter email to a 30-minute business case study call with a senior data scientist. It started with no introductions and covered a profit calculation, brainstorming around credit card ideas and the company's product, and a prediction case study. That candidate found the machine learning part fairly straightforward. The difficulty was not knowing the company's product and lease financing model well enough, and a rejection arrived about a week later.
The other Data Scientist candidate also reports an abrupt start with no introduction or warm-up. Questions covered handling missing values, feature extraction when there are too many features, and evaluating model performance. A profit problem also came up, and the interviewer guided the candidate through it. They also discussed past projects. Both candidates ended without an offer.
A Senior Data Scientist candidate describes a longer process of two case interviews on statistics, probability and data visualization, then a panel and a final call. That is a different level, so treat it as a possible signal rather than the standard Data Scientist path.
To prepare, practice clarifying terminology before you calculate, since one candidate wished they had asked more questions on the profit problem. Review how lease financing, underwriting and profitability connect to the data. Be ready to explain feature handling and model evaluation clearly. Expect a quick start and set the context yourself.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Progressive Leasing process.
I got a recruiter email inviting me to a 30-minute business case study call with a senior data scientist. No fluff—we jumped straight in with no personal introductions. It was clearly a filter round, designed to see if I could apply technical skills to actual problems the company faces.
The interview covered three main areas. First was a profit calculation question where I had to clarify some terminology with the interviewer—that should have been a hint to ask more questions upfront. Then we moved into brainstorming around credit card ideas and the company's product, which required knowing Progressive Leasing's business model and industry well. The third question was a machine learning case study on prediction, which felt relatively straightforward.
The hardest part wasn't the technical difficulty—honestly, basic stats, ML fundamentals, and understanding data science pipeline was enough. The real challenge was that I didn't know the company and product deeply enough going in. I fumbled on the last answer, and a week later I got the rejection email. The next round would have been with a more senior manager, so I was close, but not close enough. In hindsight, I should have tried their actual product before the interview and done deeper research into how lease financing works and what data matters for underwriting decisions. The interviewer wasn't trying to trick me—they just expected me to come prepared with real knowledge of what Progressive Leasing actually does.
Prep tip from this candidate
This role heavily rewards domain knowledge over algorithm expertise. Before the interview, deeply research Progressive Leasing's business model, try their product firsthand, and understand how lease financing and underwriting work—especially around credit risk, default rates, and profitability metrics. That foundation is more important than knowing advanced ML techniques.
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Topics based on recent interview experiences.
Featured question at Progressive Leasing
List out the exams sources of each student in MySQL
| Question | |
|---|---|
| Value of College | |
| Best DAU | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Rolling Bank Transactions | |
| Top Three Salaries | |
| Comments Histogram | |
| Closest SAT Scores | |
| Subscription Overlap | |
| Customer Orders | |
| Experiment Validity | |
| Employee Salaries | |
| Merge Sorted Lists | |
| Button AB Test | |
| Monthly Customer Report | |
| Cumulative Distribution | |
| Upsell Transactions | |
| First Touch Attribution | |
| Top 3 Users | |
| Last Transaction | |
| First to Six | |
| String Shift | |
| Compute Deviation | |
| Find the First Non-Repeating Character in a String | |
| Like Tracker | |
| Download Facts | |
| Prime to N | |
| Month Over Month | |
| Third Purchase |
Synthesized from candidate reports. Individual experiences may vary.
One Data Scientist candidate reports being invited by recruiter email to a short business case call. Another Senior Data Scientist candidate also reports first contact by recruiter email. Beyond this, scheduling details are not well documented, so confirm the format with your recruiter.
One candidate describes a 30-minute call that began immediately with no introductions. It reportedly included a profit calculation, brainstorming about credit card ideas and the company's product, and a prediction case study. Asking clarifying questions early may help, since terminology was unclear on the profit question.
Another Data Scientist candidate reports questions on missing values, feature extraction with too many features, and model evaluation, plus a profit problem and a discussion of past projects. The interviewer guided them through the profit problem. The structure felt scattered to that candidate, so state your approach clearly as you go.
One Senior Data Scientist candidate reports two case interviews on statistics, probability and visualization, followed by a three-person data science panel and a call with the Chief Analytics Officer. Another candidate said the next round would have been with a more senior manager. Later stages may vary by level.
