The pressure to pick something genuinely different from what I'd already covered tripped me up a bit.
Select a project that highlights your ability to make data-driven decisions under uncertainty, ideally one with clear business impact. Structure your answer around the problem, your design choices, the trade-offs you evaluated, and the outcomes, emphasizing how you balanced competing priorities.
Pro tip: Quantify the trade-offs and outcomes whenever possible (e.g., 'We sacrificed 2% accuracy for a 10x reduction in latency, which was critical for real-time pricing'). This shows you think in terms of business impact, not just technical metrics.
Briefly describe the project's goal, your role, and why it mattered to Uber. Keep it concise to leave time for the deep dive.
Explain 2-3 critical decisions you made, such as model choice, feature engineering, or data pipeline architecture, and why you made them.
For each decision, articulate the alternatives you considered and the trade-offs (e.g., accuracy vs. interpretability, speed vs. cost, scalability vs. simplicity).
Quantify the impact (e.g., improved ETA accuracy by 15%, reduced inference time by 30%) and reflect on what you would do differently.
Tie your decisions to Uber's unique challenges (e.g., real-time data, massive scale, marketplace dynamics) and how they align with Uber's culture.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pick a sub-system you mentioned briefly but that had significant design decisions, and walk through the problem it solved, the alternatives you considered, and why your choice was optimal given constraints like scale, latency, and cost. Focus on trade-offs and how you validated the decision with data or experiments.
Pro tip: Quantify the impact of your choice—e.g., 'reduced inference latency by 30%'—and acknowledge any downsides or what you'd do differently, showing self-awareness and engineering maturity.
Choose a component you only briefly mentioned but that involved non-trivial design decisions, ideally one with clear trade-offs relevant to Uber's scale.
Explain what the sub-system needed to achieve and the constraints (e.g., real-time processing, cost, data volume) that shaped your approach.
Describe 2-3 options you considered, comparing them on dimensions like accuracy, latency, scalability, and maintainability.
Explain why you chose your solution, referencing experiments, metrics, or business impact that validated the decision.
Summarize the results, any limitations, and what you would improve or change if you revisited the design.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the current system design and the nature of the 10x scale increase (e.g., data volume, user traffic, model complexity). Then systematically identify bottlenecks by walking through the data pipeline and model serving layers, prioritizing what breaks first based on resource constraints. Finally, propose concrete solutions with trade-offs, emphasizing incremental improvements and monitoring.
Pro tip: Quantify the impact: estimate the 10x scale in terms of data size, QPS, or training time, and use back-of-the-envelope calculations to show which component fails first. This demonstrates practical engineering judgment and helps prioritize fixes.
Ask questions to understand the current scale, architecture, and what '10x' means (e.g., 10x data, 10x users, 10x model size). Briefly restate the existing design to ensure alignment.
Analyze each component (data ingestion, storage, processing, training, serving) to determine which fails first under 10x load. Use estimates (e.g., memory, compute, I/O) to justify the order of failures.
For the component that breaks first, suggest specific mitigations (e.g., sharding, caching, distributed training) and discuss trade-offs (cost, complexity, latency).
Explain how fixing the first bottleneck may shift the problem to other components, and propose a holistic plan (e.g., end-to-end optimization, monitoring, auto-scaling).
Conclude with a prioritized roadmap: what to do now, next, and later, based on impact and effort. Highlight any assumptions that could change the plan.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one was more interesting than I expected.
Start by clearly defining the original success metric and the alternative metric, then systematically walk through how each stage of your data science workflow—from problem framing to modeling and evaluation—would change. Use a concrete example from your experience to illustrate the trade-offs and decision points, emphasizing how the shift in metric alters priorities and constraints.
Pro tip: Acknowledge that changing the success metric often reveals hidden assumptions and biases in the original approach, and that the best data scientists proactively consider multiple metrics to avoid overfitting to a single objective. Show that you can balance business goals with statistical rigor.
Clearly state the original success metric and the alternative metric, including how they are calculated and what they represent (e.g., revenue vs. user engagement).
Explain how the problem statement and hypotheses would change. For example, optimizing for long-term retention vs. short-term conversions leads to different feature engineering and target variables.
Describe how data collection, preprocessing, model selection, and validation would differ. Mention potential changes in sampling, label definition, and evaluation metrics.
Discuss the trade-offs between the two approaches, such as accuracy vs. interpretability, and how you would measure success and communicate results to stakeholders.
Highlight the importance of continuous monitoring and iteration, as the new metric may require different feedback loops and A/B testing strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer: I talked about a time we had significant label noise rather than missing data outright, and I'm not sure that fully landed as the same thing.
Choose a specific project where missing data forced you to make assumptions, and structure your answer using a clear framework like STAR. Focus on how you identified the gap, made reasonable assumptions, validated them with available data or domain knowledge, and quantified the impact on your analysis.
Pro tip: Emphasize that you proactively documented your assumptions and their potential impact, and that you communicated these clearly to stakeholders to align on risk—this shows maturity and transparency.
Briefly describe the project, your role, and the specific data that was missing or unavailable, highlighting why it was critical.
Explain the assumptions you made to fill the gap, and justify them using domain knowledge, historical data, or business logic.
Describe how you validated these assumptions—through sensitivity analysis, proxy data, experiments, or stakeholder feedback—and what you learned.
Discuss how you measured the impact of the assumptions on your results, such as confidence intervals or scenario analysis, and how you communicated uncertainty.
Share the final outcome, what you would do differently, and how this experience improved your approach to ambiguous data problems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.