Jane Street·Data Scientist·Technical Phone Screen·Senior

SeniorPrefer not to say

Apr 2026

Summary

Jane Street data scientist interview with a single deep-dive ML design question around DNA sequence classification. The problem was layered enough that I kept second-guessing whether they wanted a practical answer or a research-level one. Walked away unsure if I hit the right depth.

Questions Asked (1)

You have a small labeled dataset of real and fake DNA sequences, plus a much larger set of confirmed real sequences only. Design an end-to-end ML system to classify whether a new DNA sequence is real or fake, covering representation, problem framing, synthetic negatives, model choice, class imbalance, evaluation, threshold calibration, and failure modes specific to biological sequences.

System DesignTechnical Trade-offsData Modeling

Author's notes

This one sprawled in every direction.

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AI HintsAI Generated

Suggested Approach

Frame the problem as semi-supervised anomaly detection, leveraging the large set of confirmed real sequences to model the 'real' distribution and the small labeled set to calibrate a classifier. Use k-mer based representations and synthetic negatives generated by biologically-informed perturbations to augment training. Address class imbalance and threshold calibration with precision-recall analysis, and discuss biological failure modes like distribution shift and adversarial examples.

Pro tip: Emphasize that in biological sequences, the definition of 'fake' is context-dependent—clarify with stakeholders whether fake means randomly generated, shuffled, or model-generated, as this drastically changes the approach. Also, consider using a held-out set of real sequences from a different genomic region or species to test generalization.

1. Clarify problem and data

Define what 'fake' means (e.g., random, shuffled, generated by a model) and understand the data sources, labeling process, and potential biases. Confirm the availability of unlabeled real sequences and any metadata.

2. Represent sequences

Choose a representation such as k-mer frequency vectors, mismatch profiles, or learned embeddings from a pre-trained genomic language model. Consider the trade-off between interpretability and performance.

3. Generate synthetic negatives and handle imbalance

Create synthetic fake sequences by shuffling, introducing mutations, or using generative models. Combine with the small labeled fake set, and address class imbalance via resampling or class-weighted loss.

4. Model and calibrate

Train a classifier (e.g., gradient boosting, logistic regression, or fine-tuned neural network) using the real sequences as the majority class and augmented fakes as the minority. Calibrate the decision threshold using precision-recall curves to meet the desired false positive/negative trade-off.

5. Evaluate and stress-test

Use cross-validation and a held-out test set with realistic fakes. Evaluate with metrics like AUPRC, F1, and calibration error. Test for robustness to distribution shifts (e.g., different GC content, repeats) and discuss biological failure modes.

Key Points to Mention

Semi-supervised learning: use the large real set to model the real distribution (e.g., via density estimation or one-class SVM) and the small labeled set for supervised fine-tuning.
Representation: k-mer frequencies (with various k), spaced seeds, or embeddings from DNA language models like DNABERT; consider reverse-complement symmetry.
Synthetic negatives: generate by shuffling k-mers, introducing random mutations, or using generative models (e.g., GANs) to mimic potential fakes; ensure they are biologically plausible but distinct.
Class imbalance: use techniques like SMOTE, class weighting, or focal loss; evaluate with precision-recall AUC rather than ROC AUC due to imbalance.
Threshold calibration: optimize threshold on validation set to achieve desired precision/recall; consider cost-sensitive learning if false positives and false negatives have different costs.
Biological failure modes: distribution shift (e.g., new species, GC bias), adversarial sequences designed to evade detection, and the risk of overfitting to artifacts like sequencing errors or assembly gaps.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.