Technical interview prep · quant · ML · research · June 2026

When the interviewer asks “why,” most candidates freeze.I did too.

Train for second-round interviews with adversarial prep that forces you to justify every step.

39 of 150 founding seats reserved · Launches in 22 days (June 1)
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00 / The founder

Why log? Why specifically that function?

Aleksandr Zvonarev — founder of ZVSQUARED
Aleksandr Zvonarev
Founder
Built ZVSQUARED after his own WorldQuant interview

Round three at WorldQuant. I’d spent four months preparing for it.

When he asked me to explain logistic regression, I relaxed a little. Finally, something familiar.

I started answering.

Then he cut in:

“Why the log?”

And my brain just… froze.

Not because I hadn’t used logistic regression before — I had, a lot. But I’d only learned how to explain it well enough to pass interviews, not deeply enough to defend it.

I remember sitting there in silence, trying to think of anything to say.

Nothing came.

I knew right then the interview was over.

That moment changed how I think about interview prep. Most people practice polished answers. Great interviewers look for the cracks underneath them.

That’s why I built the thing I wish I’d had back then.

— Aleksandr Zvonarev
01 / The problem

You didn’t fail because the problem was hard.You failed because it wasn’t familiar.

One change—and your playbook breaks.You stall. You guess. Time runs out.

300 reps made you fast.None made you adaptable to change.

I built it because I needed it.

02 / The core interaction

Every claim earns another “why”.

You state. The examiner asks why. Each answer triggers the next why. The reasoning has to be yours, not something memorized.

Elapsed00:09:14
DEFML · Logistic regression
Task

Describe the logistic regression algorithm.

Real exchange · DEF
Why? · ×3
03 / How it works

Get tested first. Fix what breaks.

The standard pattern is study-then-test. We invert it. The mock goes first; the curriculum follows what it exposes.

  1. 01

    Get stress-tested first

    A 30-min mock opens the loop. No scaffolding. Pressure exposes what study hides.

  2. 02

    See where you broke

    Post-session review names the topics — and the layer — your reasoning broke at.

  3. 03

    Go deeper on weak areas

    Examiner sends you to the layers you need to rebuild. Targeted, not blanket.

  4. 04

    Repeat until mastery

    Re-enter the mock. Watch the gaps close. Examiner decides when you’ve proven it.

Repeat until nothing breaks.
04 / The method

Define. Understand. Apply.

The examiner decides when each layer is proven. You don’t move on until it is proven.

01
DEF · Definition

Can you actually define it?

State it precisely. Then defend every word.

02
UND · Understanding

Can you explain why it works?

Explain why it works. Derive, don’t recall.

03
PRB · Problems

Can you reason through it under pressure?

Solve under pressure. Every step justified.

05 / How the examiner works

The AI cannot think on its own.

Every problem, every checkpoint, every follow-up the examiner can ask is hand-written by the founders and verified by experts before it ships. Security gates check every reply before it reaches you — the AI is never allowed to improvise.

  1. 01

    It cannot invent.

    The AI does not write math. It does not generate questions. Every problem and every rubric step is hand-authored. There is nothing for the model to make up on its own.

  2. 02

    Security gates on every reply.

    Before any response reaches you, it’s checked against a locked library: did the AI pick from an approved question shape? Did it stay on-rubric? If anything is off, the reply is dropped.

  3. 03

    Expert-verified before launch.

    Every problem and every rubric step is reviewed by subject-matter experts before it goes live. The examiner checks your reasoning against that locked rubric — never against its own opinion.

06 / Mock interview

Thirty minutes. Real pressure.

A timed session that mirrors the real interview. Debrief at the end names the topics your reasoning broke at.

Inside the product30:00
Pillar
Difficulty

Applied reasoning under time pressure.

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39 of 150 founding seats reserved · Launches in 22 days (June 1)
Sample state · Not your accountPillar · Analysis
Sample debrief · session endedPartial

Stated Bayes’ theorem clearly and computed the posterior correctly. The breakdown occurred at conditional independence — it was assumed rather than derived from the joint distribution.

Where it broke
  • Conditional independence — must be derived, not asserted
  • Prior sensitivity — need to quantify how the posterior changes under different priors
Practice next
  • DEF
    Probability · Conditional probability
    State the definition cleanly · 2 problems
  • UND
    Probability · Conditional independence
    Derive it from the joint distribution · 3 problems
  • UND
    Probability · Independence vs. zero correlation
    Clearly distinguish the two concepts · 2 problems
  • PRB
    Probability · Posterior under prior shift
    2 problems · ~20 min
Sample state · Not your account2 gaps · 4 prescribed
07 / The difference

Why everything else fails.

When you get it wrong
Question banks

Shows you the correct solution. You read it and move on.

Zvsquared

Asks you why you got it wrong. You prove you understand the gap.

Who decides you're done
Question banks

You decide. You move on when you feel ready.

Zvsquared

The examiner decides. You move on when you've proven mastery.

What it rewards
Question banks

Pattern recognition. Recognize the type, recall the solution.

Zvsquared

First-principles reasoning. Derive under pressure, from nothing.

What breaks under pressure
Question banks

Memorized solutions collapse when one variable changes.

Zvsquared

Fundamentals hold. Variations don’t require new machinery.

The outcome
Question banks

You’ve seen a lot of problems. You freeze on the one you haven’t.

Zvsquared

You own the fundamentals. Variations stop surprising you.

08 / Common questions

Everything you might be wondering.

Candidates preparing for rigorous technical interviews — quant trading, ML, research, math grad school. Self-learners who can recognize a problem but stall when it’s rotated 30°.

09 / Early access

Early access.

Limited cohort.

June 2026.

39 of 150 founding seats reserved · Launches in 22 days (June 1)

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