Open-ended enough that I rambled a bit before landing anywhere useful.
Start by clarifying the goal: are we predicting y from x, understanding association, or causal inference? Then discuss exploratory data analysis (scatter plots, correlation) and propose a modeling approach (e.g., linear regression, splines, tree-based) justified by assumptions and trade-offs. Emphasize validation and iteration.
Pro tip: Mention that you would first check for confounding variables and consider whether the relationship is causal or just predictive, as this shows maturity beyond just fitting a model.
Ask whether the goal is prediction, inference, or causal effect. This determines the choice of model and evaluation metrics.
Visualize the relationship with scatter plots and compute correlation. Check for non-linearity, outliers, and heteroscedasticity.
Start simple: linear regression if linear trend seems plausible. If not, consider polynomial, splines, or tree-based models. Justify based on interpretability vs. flexibility.
Use cross-validation to assess performance. Check residual plots and adjust model if assumptions are violated. Consider regularization if overfitting.
Explain why you chose the model, its assumptions, and limitations. Discuss alternative approaches and when they might be better.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I blanked for a second on whether they wanted a stats answer or an ML pipeline answer.
Start by describing what you observe in the plot, then hypothesize possible causes for the overlap, and finally outline a systematic plan to investigate and address the issue. Emphasize that the right action depends on the context and goal, and propose concrete next steps such as data validation, model diagnostics, or business alignment.
Pro tip: Always tie your analysis back to the business impact and the specific ML problem—interviewers at Reddit want to see that you can prioritize actions that improve user experience or key metrics, not just technical fixes.
State what you see: two distributions overlapping, noting their shapes, central tendencies, spread, and the degree of overlap. Avoid jumping to conclusions.
List plausible reasons for the overlap, such as data leakage, label noise, class imbalance, feature scaling issues, or a mixture of subpopulations. Consider both data and model-related causes.
Rank hypotheses by likelihood and impact, then propose specific diagnostics (e.g., check data pipeline, analyze subgroups, evaluate model calibration) to confirm or rule out each.
Based on the root cause, suggest concrete steps: data cleaning, feature engineering, model retraining, threshold tuning, or collecting more data. Tie actions to business goals.
Outline how you would validate the fix (e.g., A/B test, offline metrics) and set up monitoring to detect recurrence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the two distinct cold-start scenarios—new users and new items—and their impact on Reddit's recommendation quality. Then outline a multi-pronged strategy that leverages content-based signals, exploration, and transfer learning, while balancing short-term engagement with long-term personalization. Finally, discuss evaluation metrics and iterative improvements.
Pro tip: Emphasize the importance of exploration-exploitation trade-offs and how you would measure the success of cold-start strategies using online metrics like CTR and dwell time, not just offline metrics.
Clarify what constitutes a cold-start in Reddit's context (e.g., new users with no history, new posts with no interactions) and discuss the scale and latency requirements.
For new items, use content-based features (text, images, subreddit, author) to generate embeddings and match with user profiles. For new users, use onboarding signals (e.g., selected interests, demographics) to bootstrap recommendations.
Use multi-armed bandits or Thompson sampling to explore new items efficiently. For new users, transfer knowledge from similar users via meta-learning or clustering to provide initial recommendations.
Combine collaborative filtering with content-based methods, and have fallback strategies like popularity-based recommendations or trending content when signals are sparse.
Define offline and online metrics (e.g., coverage, diversity, CTR, dwell time) and set up A/B tests to measure the effectiveness of cold-start strategies, iterating based on results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.