Palantir Data Scientist Interview Questions

1.Tell me about a time you were handed a dataset or a business problem with almost no context — no clear question, no defined success metric. How did you figure out what to actually build?

2.Tell me about a time you had a hard deadline that was genuinely at risk — not a soft milestone, but something with real consequences if you missed it. What did you do, and did you make it?

3.Walk me through a time you had to explain a modeling decision — feature selection, model choice, confidence intervals, anything technical — to an executive or customer who had no statistics background. How did you do it, and how did you know it landed?

4.Describe a project where you had to become functional in a completely new domain — logistics, genomics, fraud, whatever — in a short amount of time in order to ship something meaningful. How did you ramp, and what did you build?

5.Tell me about a time a stakeholder gave you a data science request that turned out to be the wrong question entirely. How did you figure that out, and what did you do about it?

6.Tell me about a time you were under serious pressure to ship a model or analysis and you discovered a significant flaw — data quality issue, methodological error, something that could change the conclusion. What did you do?

7.You have a Parquet dataset in Foundry with 500 million rows and a straightforward aggregation a stakeholder needs. You run it and it takes 45 minutes. What's your mental model for why, and what do you do first?

8.You're building a binary classifier to flag anomalous events in a stream of operational data. Your training set is 99% negative. What's your evaluation strategy — which metrics do you use, which do you avoid, and why?

9.You've trained a gradient boosting model that performs well in offline evaluation, but after deploying it in an AIP workflow, the business team says the outputs 'don't make sense' for certain customer segments. How do you investigate?

10.You're designing a feature pipeline in Foundry that needs to join three live operational datasets — one updated hourly, one daily, and one near-real-time — for a model that scores incoming events. Walk me through the architecture.

11.You're given a time-series of operational events from a military logistics system — think equipment failures, maintenance windows, resupply requests — and asked to build a model that predicts the next high-risk period. How do you approach this from scratch?

12.You've deployed a model in Foundry that a hospital operations team is using to prioritize patient discharge planning. Six months in, performance has degraded significantly — your AUC dropped from 0.84 to 0.71. You have full access to logs, pipeline metadata, and retraining data. Walk me through your investigation.

13.You need to design a monitoring system that detects when a model served through an AIP workflow has drifted enough to warrant a human review flag. What does that system look like?

14.Design a backtesting framework for a predictive model that will be embedded in a Foundry pipeline used by an energy company to schedule grid maintenance. What are the core components and what would you test?

15.You're building a real-time anomaly detection service for a manufacturing customer in Foundry. The sensor data comes in at 10,000 events per second, and the customer needs alerts within five seconds of an anomalous pattern. Walk me through the architecture.

16.Gotham analysts frequently run ad hoc queries over a shared entity graph that has hundreds of millions of nodes and billions of edges. Design a recommendation system that surfaces 'entities you might want to investigate next' to an analyst mid-session. What signals would you use and how do you keep latency under 500ms?

17.Design a feature store that serves both batch training jobs and real-time inference in AIP, where some features are derived from sensitive PII data that certain model teams are not authorized to use. How do you architect access control into the feature store itself?

18.Apollo ships model artifacts and pipeline code to thousands of Foundry environments, including air-gapped deployments at government agencies that can't pull from external registries. Design a model versioning and deployment system that works in both connected and air-gapped contexts without diverging into two separate systems.

19.Tell me about a time you had to convince a skeptical stakeholder — a customer, an executive, or a domain expert — to trust a model's output. What was the resistance, and how did you get there?

20.Describe a time you were the most junior person in a room and you had information or analysis that changed the direction of a decision. How did you make sure it landed?

21.Tell me about a time you were partway through a data science project and realized the original framing or scope was wrong. How did you handle reorienting the work without losing momentum or credibility?

22.Walk me through a time you had to write up a significant technical decision — a modeling approach, an architecture choice, a methodology change — for a non-data-science audience. What did you write, and how did you decide what to include or leave out?

23.Tell me about a time you made a methodological or modeling decision that you knew was ethically uncomfortable — something involving sensitive data, a population that could be harmed, a model that could be misused. How did you reason through it, and what did you do?

24.Describe a time when your analysis or model contradicted what a senior leader or a customer was already convinced was true. How did you handle it, and what happened?

25.Estimate the number of Foundry workspaces that are actively used on any given weekday. Walk me through your assumptions.

26.A Foundry pipeline that feeds a weekly executive dashboard for a manufacturing customer stops updating. The customer notices on Monday morning. How do you triage this — what's your first five minutes look like?

27.Palantir's AIP platform is growing its enterprise customer base. How would you design a metric to measure whether an AIP deployment is actually delivering business value for a customer, not just usage?

28.You're analyzing user behavior data in Foundry for an enterprise customer and you discover that a small subset of power users accounts for 80% of all workflow executions. The customer's leadership wants to 'scale what's working' to the rest of the org. How do you approach this analytically?

29.Estimate the compute cost Palantir absorbs annually from Foundry pipelines that run on stale schedules — jobs that are still triggered on a cadence but whose output no longer feeds any active downstream dataset or dashboard. Walk me through your reasoning.

30.You're a data scientist embedded with a defense agency using Gotham. An analyst asks you to build a model that predicts whether a person in the entity graph is likely to be a threat, based purely on their network connections. How do you respond?