Two candidates describe a two-round John Deere Data Scientist process. One had a fit call, then a timed coding, SQL and behavioral round. The other had two ML-fundamentals rounds with Python coding and STAR-style behavioral questions.
$98K
Avg. Base Comp
$111K
Avg. Total Comp
2 rounds
Typical Rounds
Not reported
Process Length
Both candidates describe a two-round John Deere Data Scientist process, but the second round looked different in each account, so prepare for range.
One candidate began with a 30-minute conversational fit call with a senior data scientist covering visa status and background. The final round was a strict 60-minute session mixing behavioral questions, live coding and a tech stack discussion. The coding was done without a hosted platform: the candidate shared their screen, used VS Code and a Jupyter notebook, and received no hints. The questions were a longest increasing continuous subsequence problem and a SQL aggregation of total quantity per product ID, sorted from highest to lowest. The feedback was positive, with one note to explain the approach more quickly. The candidate was still told the role had gone to someone else.
The other candidate described two rounds that both leaned on ML fundamentals and image processing: regression, classification, clustering, normal distributions and the central limit theorem. The first round included two straightforward Python coding questions and checked familiarity with pandas, seaborn and plotly. The second round pushed harder. It had one coding question the candidate had to walk through in detail, plus failure, conflict and conflicting-feedback questions, with STAR-format answers requested. This candidate felt the final round probed specifics they had not worked with directly.
A practical plan is to practice short Python and SQL problems in a plain editor while narrating concisely, review core statistics and model basics, and prepare a few STAR stories. It is also worth being ready to discuss PySpark, satellite imagery, and generative AI or NLP experience, since one candidate was asked about them.
Synthesized from 2 candidate reports by our editorial team.
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Real interview reports from people who went through the John Deere process.
I went through a two-round interview process at John Deere for a Data Scientist role. The first round focused heavily on foundational machine learning and image processing concepts. The interviewer asked deep, probing questions to assess my understanding of core topics like regression, classification, clustering, and statistics—things like normal distributions and the central limit theorem. I also got two coding questions that were fairly straightforward, designed to evaluate logical clarity and basic Python skills. They tested my familiarity with pandas, seaborn, and plotly as well.
The second round was structured similarly but pushed slightly harder on both the ML fundamentals and the coding side. I was given one coding question and asked to walk through my logic in detail. What made this round different was the addition of behavioral and case-based questions. They asked me to describe a failure I'd experienced and how I handled it, and they wanted to understand how I'd react to interpersonal challenges—things like conflict resolution within a team or handling conflicting feedback. They explicitly wanted answers in the STAR method format.
One thing that surprised me was the disconnect in expectations. The earlier screening rounds seemed to focus on general data science breadth, but the final technical round dove into specifics I hadn't worked with directly. The ML and AI landscape is huge, and it's tough to have hands-on experience with every technology out there. That mismatch made it harder to prepare effectively. Overall, the process tested both technical depth and how I communicate and collaborate, but the bar felt inconsistent across rounds.
Prep tip from this candidate
Focus your prep on foundational ML concepts—regression, classification, clustering, and statistics like distributions and the CLT—since those came up repeatedly. Be ready to explain the reasoning behind your code in detail and practice the STAR method for behavioral questions, especially around conflict resolution and handling setbacks in teams.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at John Deere
Find the longest increasing subsequence in a list of integers.
| Question | |
|---|---|
| Hurdles In Data Projects | |
| Classification and Regression | |
| Alternative Vendor Tradeoff | |
| Processing Large CSV | |
| Data Cleaning Experiences | |
| 1000 Sample Classifier | |
| Extra Delivery Pay | |
| 2nd Highest Salary | |
| Merge Sorted Lists | |
| Prime to N | |
| Bagging vs Boosting | |
| Bank Fraud Model | |
| Booking Regression | |
| Size of Joins | |
| The Brackets Problem | |
| Level Of Rain Water In 2D Terrain | |
| Covariance vs Correlation | |
| Random Forest Explanation | |
| Cyclic Detection | |
| Missing Housing Data | |
| Term Frequency | |
| Find Duplicate Numbers in a List | |
| Lasso vs Ridge | |
| Feature Store Integration | |
| Assumptions of Linear Regression | |
| Multi-Reaction | |
| RAG Strict Source Control | |
| Merge N Sorted Lists | |
| Training Instability in Neural Networks |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reports being contacted directly by a recruiter who thought their skills fit the role. That candidate was told the team wanted to hire quickly and finish in two to three rounds at most.
One candidate had a 30-minute conversational call with a senior data scientist covering visa status, background and current work. Another candidate describes a first round centered on ML and image-processing fundamentals, statistics, pandas, seaborn and plotly familiarity, and two straightforward Python coding questions.
One candidate reports a strict 60-minute round with behavioral questions, one DSA problem and one SQL problem solved live without hints in VS Code and Jupyter. Another reports one coding question with a detailed walkthrough, deeper ML fundamentals, and failure, conflict and conflicting-feedback questions answered in STAR format.
One candidate says the last 5-10 minutes covered the team's tech stack, including PySpark and satellite imagery, and whether they had generative AI and NLP experience. The other report does not mention this segment, so applicants should treat it as possible rather than guaranteed.
Both candidates report being rejected. One was told about a week after the final round that the position had been filled by someone else, after positive feedback with a note to explain coding approaches more quickly. The other did not report a reason and felt the technical expectations were inconsistent between rounds.
