The Palantir insider trading interview question: how to answer it without knowing finance

By FDE Interview Prep, a SourceStrongAI company

In this post11 sections

1. What the recruiting lead described, and what she did not
2. Learn the domain from the interviewer, out loud
3. Who the user is and what decision they make
4. The signals worth looking for
5. The data you would need, and who owns it
6. A first version an analyst can use
7. False positives, review and what not to automate
8. The same moves in any domain you don’t know
9. Questions people ask
10. Keep reading
11. More from the blog

The interviewer explains insider trading in a few plain sentences, then asks how you would detect it. You have never worked in finance, you are not sure what “material” means here, and the clock is running. This post works the scenario end to end, and the decomposition interview guide covers the round as a whole.

This scenario is on the record: Palantir’s former FDE recruiting lead, Shilpa Balaji, told First Round Review that interviewers would explain insider trading to the candidate and ask them to design a solution, including what data they would need, what they would ask the customer and what they would look for. ^[1]Source 1So You Want to Hire a Forward Deployed EngineerPublisherFirst Round ReviewSource typenews report

You do not need securities law to answer it well. The answer in five moves:

1. Learn the domain from the interviewer, out loud.
2. Name the user and the decision they make.
3. Sort the signals by how hard their data is to get.
4. Name each data source’s owner and flaw.
5. Ship a ranked queue with human review, not a verdict machine.

What the recruiting lead described, and what she did not

The source is a single article: First Round Review reported, in February 2026, that Palantir’s former FDE recruiting lead said many of Palantir’s FDE interview questions were oriented around high-level problem solving on customer problems rather than Google-style coding tests. ^[1]Source 1So You Want to Hire a Forward Deployed EngineerPublisherFirst Round ReviewSource typenews report She said the goal of these interviews was to assess both business reasoning and technical reasoning, as First Round Review reported. ^[1]Source 1So You Want to Hire a Forward Deployed EngineerPublisherFirst Round ReviewSource typenews report Hiring managers, she told First Round Review, would present a problem a customer was working on that no one had been able to solve, and ask how the candidate would solve it. ^[1]Source 1So You Want to Hire a Forward Deployed EngineerPublisherFirst Round ReviewSource typenews report

Here is what it does not say:

• That this prompt is asked today, or in any particular round.
• How long the discussion runs, or what a passing answer looks like.
• Any rubric or scoring.

Treat it as a former insider’s example, not as Palantir’s question list. The useful lesson is the shape: a domain you don’t know, explained to you on the spot, followed by “design something.” That shape transfers, so prepare for the shape, not the prompt. For a reported prompt worked minute by minute, see our Palantir decomposition interview example.

Learn the domain from the interviewer, out loud

The interviewer will give you a working definition. It might sound like this (a simplification for the exercise, not legal advice):

Insider trading is buying or selling a company’s stock while you hold important information about it that the public doesn’t have yet, or passing that information to someone who trades on it.

Your first move is to play it back in your own words and check the edges:

“Let me make sure I have it. Someone learns something that will move the price, say an acquisition, before it’s announced. They trade before the announcement, or they tell a friend who does. The information has to be important enough to matter to investors, and it has to be non-public at the time of the trade. Is that right?”

Then ask the questions that turn a definition into a system. Each one changes what you would build:

1. Who knows before the public does? Deal teams, executives, lawyers, anyone briefed on the deal.
2. When does it become public? An announcement has a timestamp. That timestamp is the pivot of the whole design.
3. What does a suspicious trade look like, as a record? Who traded, what, which direction, how much, and when.
4. Does it matter how they learned it? A banker on the deal and a stranger who overheard it on a train are different cases, because in US law the case turns largely on a duty to keep it confidential (SEC Rule 10b5-2 lists when that duty exists).

Palantir’s recruiting blog, in August 2022, told intern and new-grad candidates to think out loud and share questions and observations so interviewers can see how they approach the problem. ^[2]Source 2From Pipeline to Prospect: Insights and Advice from Palantir Recruitment (Palantir Blog; archived copy)PublisherPalantir Technologies (Palantir Blog on Medium)Source typearchived company page Learning the domain is where that pays most. Reasoning the interviewer can’t hear doesn’t help you.

Learning a concept on the spot is its own skill, and our post on Palantir’s learning interview drills it: restate, ask for one example, build the smallest version.

Who the user is and what decision they make

“Detect insider trading” is not yet a problem. It has no user. Ask:

“Before I design anything: who is the customer, and who will use this every morning?”

The answer changes what data you can see and what the output is for. The first row is a real job: FINRA Rule 3110(d) requires member firms to include in their supervisory procedures a process for reviewing trades in the firm’s own accounts and its employees’ accounts, designed to identify trades that may violate insider trading rules.

If the interviewer says “you pick,” pick one and say why:

“I’ll assume the customer is the compliance team at an investment bank that advises on mergers. Their bankers learn about deals before they’re public, so the risk is inside the building, and compliance can see both the deal list and employees’ trading accounts. The user is a compliance analyst. The decision is: which of today’s trades do I look at first, and which do I escalate?”

Our post on clarifying questions for a decomposition interview covers how to pick the questions that change your first version and assume the rest.

The signals worth looking for

Now answer “what would they look for?” Name signals, then sort them by cost.

Cheap, from records compliance already holds:

• Access. The person was on the deal team, was briefed on the deal, or the company is on the bank’s watch list.
• Timing. The trade came shortly before a price-moving announcement, not after it.
• Direction. Bought before good news, or sold before bad news.
• Process breaks. The trade wasn’t pre-cleared, or the stock was on the restricted list when they traded.

Richer, from history:

• Unusual for this person. Their first trade in this stock, larger than anything in their history, or options instead of shares.

Expensive, and later:

• Links. A relative’s account, a friend at another firm, several unconnected accounts buying the same stock before the same news.

Say it out loud like this:

“Access plus timing plus direction is the core signal, and it only needs records compliance already owns. Unusual-for-the-person needs trade history. Links between people are the strongest signal and the hardest data, so I’d leave them out of the first version.”

The data you would need, and who owns it

Every source has an owner, a lag and a flaw. Name all three.

Then name the join problems. In our method, this is where your technical reasoning shows.

• People. The employee ID in HR may not match the account holder on a broker statement. You need a mapping, and someone has to own it.
• Securities. Tickers change, especially around mergers. Join on a stable security identifier, not the ticker.
• Time. A trade minutes before an announcement and a trade minutes after it tell opposite stories, and a date alone can’t tell them apart. Convert every timestamp to UTC at load time and store one format, so a trade just before the New York close and an announcement that evening in London compare correctly.

“For each source, I’d want to know who owns it, how fresh it is, what’s wrong with it, and who says yes to access. The deal list is the one I’d ask for first, because without it I can’t tell who had access.”

In Pro, our lesson on inputs, owners and freshness turns this into a habit you can run on any case.

A first version an analyst can use

Palantir’s careers page on open-ended questions says to articulate alternatives and trade-offs, be pragmatic enough to arrive at a concrete approach, and deliver a functioning idea first, then expand it. ^[3]Source 3Palantir Careers | Navigating Open-Ended QuestionsPublisherPalantir TechnologiesSource typecompany hiring page Here, the functioning idea is not a machine learning model. It’s a ranked review queue.

“The first version is a daily list for the analyst. For every price-moving announcement, it finds trades in that company’s stock during a short window before it. Trades by people with recorded access go to the top. Each row says why it’s there: who, what, when, how long before the announcement, and what access they had. The analyst decides. The system only ranks.”

If the interviewer asks you to make it concrete, sketch the core query. On a small fictional schema, in SQLite syntax, it’s short:

-- timestamps: UTC text, 'YYYY-MM-DD HH:MM:SS'
SELECT t.employee_id, t.security_id, t.side,
       t.traded_at, a.announced_at,
       EXISTS (
         SELECT 1 FROM access AS x
         WHERE x.employee_id = t.employee_id
           AND x.security_id = t.security_id
           AND x.from_at <= t.traded_at
       ) AS had_access
FROM trade AS t
JOIN announcement AS a
  ON a.security_id = t.security_id
 AND t.traded_at < a.announced_at
 AND t.traded_at >=
     datetime(a.announced_at, '-10 days')
ORDER BY had_access DESC, t.traded_at DESC;

Four choices to say out loud:

• security_id, not ticker. Tickers change around mergers, which is exactly when this query matters.
• EXISTS, not a join, for access. Someone briefed twice on the same deal would otherwise appear twice, and the queue would double-count them. A trade can still match two announcements in the same window; I’d show it once, next to the nearer announcement.
• Only trades before the announcement. The strict < drops trades placed after the news broke.
• The -10 days window is a guess. Make it a setting the analyst can change, and say you’d tune it with them.

Why rules and not a model? You have no labels yet: nobody has marked which past alerts were real. Rules are explainable to an analyst, legal and an auditor. A model can come later, trained on the analyst’s decisions.

What the first version fakes: the access table might be a weekly spreadsheet export, and access never expires in this sketch; the real table needs an end date when the deal closes or dies. Say so, and say what you’d automate next.

False positives, review and what not to automate

The queue will flag innocent trades, and a strong answer plans for them. Innocent causes to name:

• The trade was pre-cleared by compliance.
• It was part of a trading plan set up in advance.
• It was placed in a discretionary managed account, where a manager trades and the employee doesn’t choose the trades.
• It was an automatic dividend reinvestment.

Don’t delete these. Suppress them with a visible reason an auditor can see.

Then design the review step:

• The analyst closes each alert with a reason, asks the employee for an explanation, or escalates to legal.
• Every decision is logged with who made it and when.
• Those decisions become the labels a later model needs.

How you’d know it works

“How would you measure it?” is the most predictable follow-up to a first version. Have a line ready:

“I’d track what share of alerts the analyst escalates rather than closes, how long each alert takes to close, and whether any case reached us another way, from a regulator inquiry or a tip, that the queue missed. If most alerts close in seconds as obvious noise, the suppression rules come next.”

And say what you would not automate:

“The system never accuses anyone, freezes an account or reports to a regulator. Those are human decisions with legal consequences. And I wouldn’t read employees’ messages in the first version. That needs legal sign-off and a clear reason, and the trade data alone gives the analyst a useful queue.”

Expect a follow-up that pokes at the edges: “What if the banker tips a friend who trades at a different broker?” A good answer admits the limit:

“This system can’t see that account. That’s the exchange’s or the regulator’s view, not the bank’s. What the bank can do is make its own records easy to hand over when asked: who was briefed on the deal, and when. I’d name that as out of scope for the first version rather than pretend to cover it.”

The same moves in any domain you don’t know

Strip away the finance and this is a detect-and-respond problem: something goes wrong inside noisy data and is found too late. In Pro, the lesson on problem shapes shows how to recognize that shape in a hospital, a factory or a payments company, and which first questions it needs. The Pro lesson Worked cases: healthcare and finance hands you a merchant-fraud case with half its board filled in: the same shape, with money at stake.

In the same First Round Review article, an FDE hiring lead says an FDE isn’t somebody who brings a playbook, and that FDEs are outcome-oriented, independent thinkers. ^[1]Source 1So You Want to Hire a Forward Deployed EngineerPublisherFirst Round ReviewSource typenews report You can’t memorize the domain. You can rehearse the moves.

Mistakes to avoid, and the fix for each:

• Reciting law. Fix: one playback sentence, then move to the user.
• Starting with a model. “I’d train a classifier” with no labels. Fix: rules first, labels from review.
• No owners. Data listed as if it were already in one place. Fix: name who says yes to each source.
• Automating the verdict. Fix: the system ranks; people decide.

To see the full method on one page, read the free lesson on the open-ended round and the method. Then run the same moves on faster small-business loans, a free banking question with a model answer.

The free practice case drops you into a domain you probably don’t know either: a city whose building permits take months, and a mayor who has already promised an AI fix. The AI customer answers only what you ask. Sign in with a Google account or an email link to start. That is exactly this post’s moves, out loud. The rest of the decomposition module, including the lessons on inputs and problem shapes, comes with Pro, which starts with a 7-day free trial. See Pro for details.

Questions people ask

What did First Round Review report about the Palantir insider trading interview question?

Palantir’s former FDE recruiting lead told First Round Review that interviewers would explain insider trading to the candidate and ask them to design a solution: what data they would need, what they would ask the customer and what they would look for. It is her example scenario, not a published question list.^[1]Source 1So You Want to Hire a Forward Deployed EngineerPublisherFirst Round ReviewSource typenews report

Do I need to know finance for the insider trading scenario First Round Review described?

No. In the example Palantir’s former FDE recruiting lead gave First Round Review, the interviewer explains insider trading first, and she said these interviews aimed to assess business reasoning and technical reasoning. Ask sharp questions about the domain instead of reciting securities law.^[1]Source 1So You Want to Hire a Forward Deployed EngineerPublisherFirst Round ReviewSource typenews report

What is a good first version for detecting insider trading?

A ranked queue for a compliance analyst: trades placed shortly before a price-moving announcement by people linked to the company, each shown with the reason it was flagged and the records behind it. The analyst decides; the system only ranks what to look at first.

Keep reading

Guides

• The decomposition interview: how to break down a vague customer problem

Company guides

• Palantir

Lessons

• The FDE decomposition interview: the method on one page
• Inputs: what data exists, who owns it, how fresh it is
• Worked cases: healthcare and finance
• Problem shapes: recognizing a case you have already solved

Questions

• Tell me about a time you had to become useful in an unfamiliar domain within a couple of weeks. How did you learn it?
• A bank wants to approve small-business loans faster without taking more risk. How would you break this down?
• A city transit agency says its buses are unreliable and riders are leaving. You have a week with their team. How do you approach it?

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