Zendesk Data Scientist interviews reported by candidates include an HR screen, a short hiring manager resume review, and a timed AI onboarding case study. Another candidate recalls a question on how transformers work.
$140K
Avg. Base Comp
$227K
Avg. Total Comp
3
Typical Rounds
Not reported
Process Length
The two Zendesk Data Scientist reports point to an applied-AI emphasis rather than a heavy modeling or coding screen. One candidate, who interviewed for a new Pune team focused on accelerating AI capabilities, described three rounds: an HR screen, a 30-minute hiring manager conversation, and a final case study. The hiring manager round was mostly a resume walk-through, and practical AI implementation experience seemed to matter more than deep theory.
The final round was the distinctive one. The candidate had to build an onboarding plan for a new customer deploying an AI agent for customer experience, condense it into a few slides, and present it within 30 minutes to a panel of two or three people. Structure, clarity of the rollout, and composure under time pressure mattered more than technical depth in that account. That candidate was declined and found the process fairly shallow for a data science role.
A separate, older report recalls a conceptual question on how transformers work. That candidate found it easy to answer superficially and hard to explain clearly from memory, and the outcome was unclear. The two reports differ in period and focus, so prepare for both: rehearse a concise customer-facing AI deployment plan, and be ready to explain modern deep learning ideas such as transformers in plain language. Be prepared to describe past AI projects in terms of customer impact, rollout steps, and results.
Synthesized from 2 candidate reports by our editorial team.
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| Forecasting Revenue | |
| Smart Home Security Launch | |
| Why Do You Want to Work With Us | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Top Three Salaries | |
| Merge Sorted Lists | |
| Comments Histogram | |
| Closest SAT Scores | |
| Upsell Transactions | |
| Subscription Overlap | |
| Customer Orders | |
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| Monthly Customer Report | |
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| Experiment Validity | |
| Download Facts | |
| Prime to N | |
| Top 3 Users | |
| Random SQL Sample | |
| String Shift | |
| 500 Cards | |
| Compute Deviation | |
| Last Transaction | |
| Manager Team Sizes | |
| Paired Products | |
| Employee Salaries (ETL Error) | |
| Largest Salary by Department |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reports an initial HR screening that was straightforward and focused mostly on basic fit. Candidates should typically be ready to summarize their background and explain why they want a data science role at Zendesk.
The same candidate describes a 30-minute conversation that was essentially a resume walk-through and a check that their background matched the role. Knowledge of AI implementation seemed to count for more than deep theory, so prepare concrete examples of applied AI work.
The final round reported was a case study: design an onboarding plan for a new customer deploying an AI agent for customer experience. The candidate condensed it into a few slides and presented within 30 minutes to a panel of 2-3 people. Expect to be judged on structure and clarity under time pressure.
A separate, older report recalls a technical question on how transformers work, where a surface answer was easy but a clear explanation from memory was harder. This may not appear in every process, but it is worth preparing a first-principles explanation of the core components.
