Uber·Data Scientist·Onsite - Product Sense / Strategy·Senior

Senior

Jun 2026

Summary

Uber data scientist interview with a pretty gnarly operations/leadership case about a broken candidate funnel. The question was more product-strategy-meets-program-management than anything I expected for a DS role, and I spent way too long on the metrics side before realizing they wanted concrete ownership and governance answers too.

Questions Asked (1)

You run a program where candidates complete paperwork first, then must take a specific qualifying action. Most drop off after the paperwork and never take that action. Given a 14-day deadline and limited reviewer capacity, how would you: diagnose the top drop-off causes within a week, redesign incentives or process to get 60%+ of paperwork-complete candidates to finish the qualifying action, protect against low-quality gaming of that action, and set up clear ownership and escalation paths across teams?

Product Analytics & MetricsCross-functional AlignmentRoadmap Prioritization

Author's notes

This one sprawled in a way I wasn't ready for.

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AI HintsAI Generated

Suggested Approach

Structure your answer around a data-driven diagnostic funnel, then propose targeted interventions with measurable goals, and finally establish governance to ensure quality and accountability. Emphasize quick wins within the 14-day deadline while building sustainable processes.

Pro tip: Frame your answer around Uber's core principles: data-driven decision making, operational efficiency, and cross-functional collaboration. Show you understand the balance between speed and quality in a high-stakes environment.

1. Diagnose Drop-off Causes

Within a week, analyze funnel data to identify where and why candidates drop off. Segment by demographics, channel, and behavior; conduct surveys or interviews for qualitative insights.

2. Redesign Incentives and Process

Based on diagnosis, implement targeted incentives (e.g., reminders, rewards) and streamline the qualifying action to reduce friction. Set a goal to achieve 60%+ completion.

3. Protect Against Gaming

Introduce validation checks, random audits, and quality thresholds to ensure the qualifying action is genuine. Use anomaly detection to flag suspicious patterns.

4. Establish Ownership and Escalation

Define clear roles across teams (e.g., product, ops, data) with a RACI matrix. Set up escalation paths for issues like reviewer capacity or quality breaches.

5. Monitor and Iterate

Track completion rates, quality metrics, and reviewer load in real-time. Use A/B testing to refine interventions and ensure sustained improvement.

Key Points to Mention

Funnel analysis and cohort segmentation to pinpoint drop-off
Behavioral nudges (e.g., reminders, deadlines) and incentive design
Quality assurance mechanisms like audits and anomaly detection
Cross-functional collaboration and clear ownership (RACI)
Scalable solutions that balance speed and quality within constraints
Metrics for success: completion rate, quality score, time-to-completion

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