John Deere Data Scientist interview typically runs 2 rounds: recruiter fit call, final interview. It usually takes 2-3 weeks and is fast-moving with a hard deadline.
$97K
Avg. Base Comp
$115K
Avg. Total Comp
2
Typical Rounds
1-2 weeks
Process Length
Our candidates report that John Deere’s bar is less about flashy algorithms and more about whether you can work through a problem cleanly, on your own, under real interview conditions. In the experience we saw, the interviewer explicitly said they would not prompt or nudge, which makes independent problem solving the clearest signal in the process. That means they are watching not just for a correct answer, but for how you structure your thinking, move from ambiguity to a workable plan, and stay concise while you do it.
A recurring theme is that John Deere wants data scientists who can operate across the stack without getting lost in it. The candidate was asked about PySpark, satellite imagery, and whether they had experience with generative AI and NLP tools, which suggests the team is looking for practical familiarity with modern data workflows, not just textbook DS knowledge. We also see a strong emphasis on applied SQL and straightforward coding tasks, paired with behavioral prompts about conflict, prioritization, and handling complex challenges. The non-obvious make-or-break factor here is often how efficiently you explain your approach; the feedback in this case was positive, but the candidate was still told they spent too long on the coding explanation. That tells us John Deere is screening for people who can be clear, fast, and self-directed in a business setting.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the John Deere process.
I found out about the opening when a recruiter reached out to me directly, saying my skill set might be a good fit. The process moved quickly — they were in a hurry to hire and wanted to wrap things up in two to three rounds at most. The first round was a 30-minute fit call with a senior data scientist on the team. They wanted to understand my visa status, my background, what I was currently doing, and get a general sense of my skills. It was conversational and low-pressure.
The second round, which turned out to be the final round, was a packed 60-minute session with a hard deadline — they were strict about it to be fair to all candidates. It covered three areas: behavioral, technical, and a tech stack discussion. For behavioral, I was asked about a time I faced conflict with a team member, a time I solved a complex challenge, and how I prioritize when working on multiple tasks simultaneously. The technical portion included one DSA question — finding the longest increasing continuous subsequence of an array — and one SQL question involving aggregating total quantity per product ID ordered from highest to lowest. There was no hosted coding platform; I had to share my screen, open VS Code and a Jupyter notebook, and write solutions from scratch after the questions were pasted into the Teams chat. They told me upfront they wouldn't prompt or nudge me — they just wanted to see how I approached problems independently. Finally, the last 5–10 minutes were a discussion about their tech stack, including PySpark and satellite imagery, and whether I had experience with generative AI and NLP tools.
The feedback I received afterward was positive overall — they called it a strong performance. The one piece of constructive criticism was that I took a bit too long explaining my approach on the coding question and could be quicker there. Unfortunately, about a week later I was told they had filled the position with someone else. It was a tough outcome, but the experience gave me a clear picture of what technical interviews at companies like John Deere look like, and using Interview Query's company-specific guides to prepare was genuinely helpful — the content was pretty close to what I actually encountered.
Prep tip from this candidate
Expect a fast-moving process with a conversational recruiter screen followed by a tightly timed final round. Practice solving DSA and SQL problems live in VS Code/Jupyter without hints, and be ready to discuss PySpark, satellite imagery, and how you’ve used generative AI/NLP tools.
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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 | |
| Prime to N | |
| Bagging vs Boosting | |
| Bank Fraud Model | |
| Booking Regression | |
| The Brackets Problem | |
| Size of Joins | |
| Level Of Rain Water In 2D Terrain | |
| Covariance vs Correlation | |
| Random Forest Explanation | |
| Missing Housing Data | |
| Find Duplicate Numbers in a List | |
| Lasso vs Ridge | |
| Assumptions of Linear Regression | |
| Multi-Reaction | |
| Merge N Sorted Lists | |
| Training Instability in Neural Networks | |
| Bias vs. Variance Tradeoff | |
| Offer Matching API Design | |
| Data Preparation for Imbalanced Data | |
| Pizza No Show | |
| Overfit Avoidance | |
| Loan Model |
Synthesized from candidate reports. Individual experiences may vary.
A recruiter reached out directly to the candidate and said their background looked like a potential fit for the role. This first contact was used to gauge interest and confirm basic alignment before moving into interviews.
The first interview was a conversational fit call with a senior data scientist on the team. They asked about visa status, current role, background, and general skill set to assess overall fit for the team and role.
The final round was a tightly timed session covering behavioral, technical, and tech stack topics. It included behavioral questions about conflict, solving complex problems, and prioritization, plus a DSA problem and an SQL aggregation question solved live by screen sharing in VS Code and Jupyter Notebook without a coding platform or interviewer hints. The last portion focused on the team’s stack, including PySpark, satellite imagery, and experience with generative AI and NLP tools.