Data Scientist Interview Prep

An interview prep path for data science loops. Built on statistics and probability, machine-learning concepts, and experimentation (A/B testing), grounded in SQL and Python, with the system-design and behavioral rounds product data scientists face. Lighter on algorithmic coding than a SWE path, heavier on inference and experiment design.

Data ScientistMid~50h5 sections10 items

Section 1 of 5

Statistics and probability

The backbone of every data science loop: distributions, hypothesis testing, p-values, confidence intervals, and the Bayesian reasoning interviewers probe. Anchor these first.

01MCQStatistics questions (30 suggested)Multiple choice category

Section 2 of 5

Machine learning concepts

Bias-variance, regularization, the right evaluation metric for the problem, and the intuition behind the core algorithms - the conceptual ML screen most DS loops include.

01MCQMachine Learning questions (30 suggested)Multiple choice category

Section 3 of 5

Experimentation and A/B testing

Product data science runs on experiments. Master experiment design, sizing and power, and the pitfalls (peeking, novelty, SRM, network effects) interviewers love to test.

01MCQA/B Testing questions (20 suggested)Multiple choice category

Section 4 of 5

SQL and Python foundations

The daily tools. SQL for pulling and shaping data, Python for analysis. Pair these MCQs with the SQL Playground for hands-on query practice.

Section 5 of 5

ML system design and behavioral

Senior DS loops add applied design (serving models, analytics pipelines) and behavioral rounds that screen for going deep and acting under ambiguity.

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Three role-targeted paths are live: Backend, SRE / DevOps, and ML Engineer. More are on the way - if you have a role you want covered, let us know.

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