One Verizon Data Scientist candidate reported a three-round process covering Python and SQL coding, machine-learning discussion, stakeholder-focused behavioral questions, and a director conversation touching on GenAI.
$121K
Avg. Base Comp
$157K
Avg. Total Comp
3 rounds
Typical Rounds
2-4 weeks
Process Length
For Verizon Data Scientist interviews, the available candidate report describes three distinct conversations rather than a single all-purpose technical screen. The first combined Python and SQL coding with machine-learning questions. The coding examples were approachable: a Python palindrome problem and SQL using joins and group-bys. That makes clear, correct implementation and the ability to explain query logic more relevant preparation than assuming an advanced algorithmic exercise.
The next discussion was with two associate directors and went further into technical questions and behavioral examples about working with stakeholders. Prepare a concise story about turning an analytical question into a decision with nontechnical partners, including how you handled tradeoffs or communicated results. The final director conversation was described as a GenAI/general discussion, so be ready to discuss your experience and judgment in that area without assuming a prescribed system-design format.
Verizon’s data and analytics work sits alongside AI and machine-learning activity, which makes the reported mix of coding, ML, and stakeholder communication relevant role context. This guide is based on one candidate report, so unreported screens, question depth, and scheduling may differ.
Synthesized from 1 candidate report by our editorial team.
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3 rounds
Questions asked: Python leetcode easy - check if palindrome SQL - easy, group bys and joins Typical machine learning interview questions - pros/cons to different methods, working with missing data, etc.
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Featured question at Verizon
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| Question | |
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| Prime to N | |
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Synthesized from candidate reports. Individual experiences may vary.
One candidate reported an opening round that combined Python and SQL coding with machine-learning questions. The reported examples were a palindrome check in Python and straightforward SQL joins and group-bys; ML discussion included method tradeoffs and handling missing data.
The candidate then met with two associate directors for more technical questions plus behavioral discussion about working with stakeholders. Candidates may benefit from pairing technical explanations with specific examples of cross-functional collaboration.
A final conversation with another director was described as a GenAI/general discussion. The report does not specify a fixed prompt or evaluation format, so candidates should be prepared to discuss their relevant experience and perspective clearly.
