Palantir Technologies Data Scientist interview typically runs 5 rounds: recruiter screen, technical screen, decomp/case, behavioral, and exec or hiring manager chat. It usually takes a few weeks and is highly structured, with a strong emphasis on ambiguous problem solving.
$165K
Avg. Base Comp
$183K
Avg. Total Comp
4-6
Typical Rounds
3-5 weeks
Process Length
We’ve seen Palantir care less about polished answers and more about whether candidates can turn a messy prompt into a workable plan. Across both experiences, the standout signal was structured problem solving under ambiguity: one candidate described a huge vaccine-distribution prompt with unclear inputs and operational constraints, while another was pushed to design a personalized music recommendation service and defend every tradeoff and edge case. In both cases, the interviewers kept pressing on what hadn’t been explicitly covered, which suggests they’re testing whether you can build a complete mental model, not just sketch a high-level idea.
A recurring theme is that Palantir wants candidates to connect analysis to real users and workflows. The strongest feedback came from the candidate who framed the decomp round around data inputs, ontology, and decision-making for an end user, and from the one who noted the data case was about messy, siloed client data becoming useful for non-technical stakeholders. That tells us the bar is not just technical correctness; it’s whether your reasoning can survive contact with an operational environment. We’ve also seen that behavioral answers matter when they show ownership and handling pushback, because client-facing judgment appears to be part of the evaluation, not an afterthought.
Synthetized from 2 candidates reports by our editorial team.
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Real interview reports from people who went through the Palantir technologies process.
the process was pretty structured and started with a recruiter screen, then a 45 minute chat with a DS, and after that an onsite loop with four rounds. the early DS conversation was mostly about past projects and how i navigate ambiguity, so it felt more like a discussion of how i think than a deep technical grilling. after that, the onsite got much more Palantir-specific: one decomp round, one data case, one behavioral, and an exec chat, plus a review of the technical interviews at the end.
the decomp round was the hardest part by far and basically the filter. they gave me a huge, vague prompt around something like optimizing vaccine distribution across a region with poor infrastructure, and i had to work from scratch through the data inputs, ontology, and operational workflow. it was less about having a memorized answer and more about whether i could structure an ambiguous problem in a way that would actually help an end user make a decision. the data case was more about messy, siloed client data and how to turn that into something useful for non-technical stakeholders. the behavioral round was standard but still important, mostly around handling client pushback and taking ownership. overall it felt very different from a typical big tech loop because the emphasis was on reasoning through a messy real-world problem and tying the tech back to business outcomes. i ended up getting the offer, and the biggest prep takeaway is to practice framing vague prompts out loud and thinking in terms of workflow and decision-making, not just data structures.
Prep tip from this candidate
Practice structuring a vague, real-world prompt out loud around data inputs, ontology, and operational workflow, since the decomp round was the main filter. Also be ready to explain how you’d make messy client data useful for a non-technical stakeholder, not just how you’d analyze it.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Palantir technologies
Given two sorted lists, write a function to merge them into one sorted list.
| Question | |
|---|---|
| Button AB Test | |
| Find the Missing Number | |
| Over-Budget Projects | |
| Network Experiment Design | |
| One Element Removed | |
| Detecting Firearm Sales | |
| Level Of Rain Water In 2D Terrain | |
| Find Duplicate Numbers in a List | |
| Hurdles In Data Projects | |
| Target Indices | |
| Testing Price Increase | |
| Recruiting Leads | |
| Data Preparation for Imbalanced Data | |
| Words in Encrypted String | |
| Subway Machine Learning Model | |
| Three Indexes Adding Zero | |
| Green Dot | |
| Shortest Path Algorithms | |
| Facebook Job Board Design | |
| Generating Discover Weekly | |
| Why Do You Want to Work With Us | |
| Friends vs Posts | |
| Fixed-Length Arrays: Deletion | |
| Your Strengths and Weaknesses | |
| Data Cleaning Experiences | |
| LRU Cache 1 | |
| Marketing Dollar Efficiency | |
| Statistically Significant Test | |
| Delivery Online |
Synthesized from candidate reports. Individual experiences may vary.
An initial recruiter call to review your resume, discuss your background, and cover basic behavioral questions like why you want to work at Palantir. This stage is usually conversational and helps assess fit before the technical rounds.
A coding-focused interview, often in a LeetCode medium/hard style. One candidate received a classic trapped rain water problem on an integer matrix, suggesting the screen tests clean problem-solving under time pressure.
A discussion with a data scientist focused on past projects, how you navigate ambiguity, and how you think through problems. This round feels more like a conversation about reasoning and experience than a deep technical grilling.
A Palantir-specific round centered on structured problem solving and decomposition. Candidates are given an ambiguous business or product problem, such as vaccine distribution or a personalized music recommendation service, and are expected to work through data inputs, tradeoffs, edge cases, and an end-to-end solution.
A structured onsite loop with multiple interviews that can include decomp, a data case, behavioral questions, and an executive chat. The emphasis is on turning messy client data or vague business problems into actionable workflows for non-technical stakeholders.
A final discussion that may happen after the onsite loop, followed by a review of the technical interviews. This stage appears to be part of the final decision process rather than a separate heavily technical assessment.