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Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

What difficulty level is this interview question?

This is a medium difficulty Machine Learning question, commonly asked during Technical Screen rounds at Jane Street.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Jane Street during technical interviews.

Build a DNA authenticity classifier | Jane Street Interview Question

Quick Overview

This question evaluates competencies in semi-supervised and anomaly-detection paradigms, realistic synthetic data generation and augmentation, sequence representation and model selection for biological sequences, and robust evaluation practices including handling class imbalance and leakage.

You are given DNA sequences composed of the characters A, C, G, and T. The task is to predict whether a sequence is real biological DNA or a fake / synthetic negative example.

Available data:

A small labeled dataset containing both real and fake DNA sequences.
A large additional training dataset containing only real DNA sequences.

Discuss how you would approach this problem. In particular:

How would you frame the learning setup given that most of the extra data are positive examples only?
How would you generate or augment fake DNA samples without creating unrealistic artifacts that make the classification problem artificially easy?
How would you represent DNA sequences and choose a model, from simple baselines to more advanced sequence models?
How would you evaluate the classifier, choose metrics, and avoid leakage from duplicate or highly similar sequences?
What risks would you watch for, such as class imbalance, overfitting, calibration, and distribution shift between synthetic negatives and real-world fake DNA?

Build a DNA authenticity classifier

Quick Overview

Solution

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