Reddit·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Reddit ML Engineer interview with a hands-on data prep coding question. The whole thing was basically a Jupyter notebook exercise where you had to wrangle a JSON dataset into something model-ready, which felt more like a data engineering warmup than a proper ML interview.

Questions Asked (1)

Given a JSON dataset with fields for hours spent reading different post categories, a current post category label, and a binary click outcome, load it into a pandas DataFrame, enforce correct data types, one-hot encode the categorical field, validate the schema, and confirm there are no missing values or class imbalance issues.

Data ModelingTechnical Trade-offsProduct Analytics & Metrics

Author's notes

This looked straightforward and I almost got complacent.

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AI HintsAI Generated

Suggested Approach

Walk through a reproducible pipeline: load the JSON into pandas, enforce dtypes with explicit casting, one-hot encode the categorical field, then validate schema and check for missing values and class imbalance. Emphasize why each step matters for model reliability and how you'd handle issues like unseen categories or imbalanced classes.

Pro tip: Mention that you'd use a schema validation library like pandera or Great Expectations to catch silent data issues early, and discuss how class imbalance might affect your choice of evaluation metric (e.g., PR-AUC over accuracy).

1. Load and inspect data

Read the JSON into a pandas DataFrame and inspect the first few rows, dtypes, and shape to understand the raw structure.

2. Enforce correct data types

Explicitly cast columns to their intended types (e.g., float for hours, category for post category, int8 for binary click) to avoid silent errors.

3. One-hot encode categorical field

Apply one-hot encoding to the post category column, handling unseen categories and considering sparse output for high cardinality.

4. Validate schema and missing values

Check that all expected columns are present with correct types, and confirm there are no missing values using isnull().sum().

5. Check class imbalance

Compute the distribution of the binary click outcome and assess imbalance (e.g., via value_counts or a bar plot), noting potential need for resampling or class weights.

Key Points to Mention

Use of pandas dtypes (e.g., category, int8) for memory efficiency and correctness
One-hot encoding with pd.get_dummies or sklearn's OneHotEncoder, and handling of unseen categories
Schema validation tools like pandera or Great Expectations for robust pipelines
Missing value detection and imputation strategies
Class imbalance metrics (e.g., precision-recall AUC) and mitigation techniques (resampling, class weights)
Reproducibility and automation of the data validation steps

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