Candidate reports for ADP data science roles describe a friendly, fit-focused process. One intern recalls a 30-minute conversational phone screen. A senior candidate recalls an independent technical challenge and a resume-driven technical interview.
$115K
Avg. Base Comp
$130K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
ADP data science interviews, as candidates describe them, are conversational and centered on your own background rather than puzzle-style questions. One intern candidate reports a 30-minute phone screen that began with a resume walkthrough and moved to open-ended questions about why they focused on AI and what they wanted in a role. They recall no live coding or whiteboarding and ended with an offer.
The senior candidate describes a longer path: an HR screen, a technical challenge solved independently, then a technical interview. That interview covered how they approached the challenge and probed their resume. The questions focused on the evaluation metrics used in past projects, so they had to explain how they judged whether their work succeeded. They were also asked how they structured a RAG-based chatbot, including retrieval, context handling and answer quality. This candidate did not receive an offer, but described the process as organized, respectful and friendly.
These are two individual reports at different levels, so expect the format to vary by role. Prepare a clear story about your career choices and goals, and be ready to defend the metrics, trade-offs and design decisions in each project you list. If you have built retrieval or LLM-based applications, rehearse explaining how you measured quality. Cultural fit and genuine interest in the work came up in both accounts.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Adp process.
I had an HR interview and then a first technical interview that was very relaxed. The talk started lighter, with questions about what I did day to day and why I had agreed to talk, and then moved into examples of my work. They asked me what the hardest project I had ever handled was and also how I would go about monitoring a model in production, so I felt they wanted to understand both my practical experience and my thinking about operating models. It was not a heavy algorithm interview, more a conversation about decisions I made and how I work in general.
From what I saw, the process had 2 rounds. The first was technical, focused mainly on writing SQL queries and on Python programming questions. The second mixed a technical part with aptitude questions, so it was not just data science content, but also a test of how I thought and answered under pressure. In my case, the next steps would be a technical challenge and maybe another interview, if necessary, but I ended up not advancing. Overall, I would say it is worth arriving sharp on SQL and Python and also having a clear answer about monitoring a model in production, because that came up very directly.
Prep tip from this candidate
Practice SQL and Python with a focus on practical questions about writing queries and programming, because that was the center of the first round. It is also worth preparing an objective explanation of how you would monitor a model in production, since that question came up explicitly.
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Synthesized from candidate reports. Individual experiences may vary.
Both candidates describe an early conversational screen. The intern recalls a 30-minute phone call that began with a resume walkthrough and moved to motivation questions, such as why they chose AI and what they wanted in a role. The senior candidate describes standard background and motivation questions. Neither mentions live coding at this stage.
The senior candidate reports being given a technical problem to solve on their own after the HR screen. The intern does not mention this step, so it may apply only to some roles or levels. The candidate does not describe the format in detail, so keep your work easy to explain, since it was discussed again later.
The senior candidate reports a technical interview covering how they approached the challenge, followed by resume questions about the metrics used to evaluate past projects. They were also asked how they structured a RAG-based chatbot, covering retrieval, context handling and answer quality. They describe interviewers as friendly and interested in their reasoning more than trick questions.
