State Farm Data Scientist interview typically runs 3 rounds: one-way HireVue, live ML interview, panel final. The process is fast, often moving within days, and is front-loaded with a one-way video screen.
$94K
Avg. Base Comp
$110K
Avg. Total Comp
3-4
Typical Rounds
1-3 weeks
Process Length
Our candidates report that State Farm is unusually focused on whether you can explain core modeling ideas cleanly, not just name-drop them. The questions that keep showing up are the kind that expose real understanding: multicollinearity, bias-variance tradeoff, logistic regression assumptions, and how to handle imbalanced data. Even the lighter prompts, like explaining a p-value to a layman, point to the same expectation — they want someone who can translate technical judgment into plain language without hand-waving.
A recurring theme is that the company seems to value practical ML intuition over polished performance. We’ve seen candidates asked to compare XGBoost vs. Random Forest and to discuss unsupervised learning, which suggests they’re checking whether you understand when a model choice makes sense in context. The insurance angle also matters: prompts like “Insurance Leads” hint that they care about how you think about prediction problems in a business setting, not just textbook definitions.
What makes this process feel selective is the combination of technical depth and limited room to improvise. One candidate described the experience as front-loaded and camera-heavy, with little recruiter guidance and no feedback afterward. That means the signal likely comes from clear, concise explanations under pressure — especially when you have to defend modeling choices without a live back-and-forth to rescue a vague answer.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the State Farm process.
The part that stood out most to me was how much of the process was front-loaded into a one-way HireVue. I applied online and got an interview invite the next day, which was fast, but the format was a little awkward: you record yourself answering 5-6 questions, you get about 30 seconds to think, and you only have one chance to re-record each response. It felt less like a conversation and more like trying to perform well on camera without any real back-and-forth, and the video setup itself was not great. I also wish there had been more recruiter contact up front, just to understand how the role was being evaluated.
The HireVue questions were mostly technical and centered on machine learning and predictive modeling, with a few fit questions mixed in. I was asked to introduce myself and walk through my work experience, explain why I was a good fit, and then answer more technical prompts like multicollinearity and how to address it. The other reviews I saw lined up with that general theme: questions like what unsupervised machine learning is and what assumptions are needed for logistic regression. After the HireVue, the process seemed to continue into a live ML interview with two data scientists, and then a panel-style final round, but I didn’t get that far. I heard back the day after submitting the HireVue that they were not moving forward, and the email said they don’t provide feedback because multiple factors go into the decision. Overall it felt pretty selective for a process that asked for a lot of time up front, so I’d prep specifically for ML fundamentals and be comfortable explaining modeling concepts clearly and concisely on camera.
Prep tip from this candidate
Focus on ML fundamentals that came up in the HireVue and live rounds: be ready to explain unsupervised learning, logistic regression assumptions, and multicollinearity in plain language. Also practice giving concise, camera-friendly answers to fit questions like why you want the role and how your background matches predictive modeling work.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at State Farm
Write a function to determine whether or not two rectangles overlap.
| Question | |
|---|---|
| P-value to a Layman | |
| Hurdles In Data Projects | |
| Insurance Leads | |
| Assumptions of Linear Regression | |
| Digitizing Student Test Scores | |
| Bias vs. Variance Tradeoff | |
| Data Preparation for Imbalanced Data | |
| Multicollinearity in Regression | |
| Double Card Value | |
| Different Card | |
| Xgboost vs Random Forest | |
| Area Under the ROC Curve | |
| Your Strengths and Weaknesses | |
| Bias Variance Tradeoff | |
| 2nd Highest Salary | |
| Employee Salaries | |
| Top Three Salaries | |
| Experiment Validity | |
| Bagging vs Boosting | |
| Random SQL Sample | |
| Booking Regression | |
| Always Excited Users | |
| Size of Joins | |
| Total Spent on Products | |
| WAU vs Open Rates | |
| Delivery Estimate Model | |
| Instagram TV Success | |
| Group Success | |
| Covariance vs Correlation |
Synthesized from candidate reports. Individual experiences may vary.
Candidates apply online and may receive an interview invitation very quickly, sometimes the next day. In the reported experience, this was the first contact point before any recruiter conversation.
This stage is a recorded video interview with 5-6 questions and about 30 seconds to think before answering each prompt. Questions are a mix of fit and technical machine learning topics, including self-introduction, why you are a good fit, multicollinearity, unsupervised learning, and logistic regression assumptions.
Candidates who move forward may have a live technical interview with two data scientists. Based on the experience shared, this round appears to focus on machine learning and predictive modeling fundamentals in a more interactive format.
The process may conclude with a panel-style final round. The available experience does not include details of the questions asked, but it is described as a later-stage interview after the live ML round.
