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This is a hard difficulty Machine Learning question, commonly asked during HR Screen rounds at Intuit.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Intuit during technical interviews.

Decide when to model courier ETA | Intuit Interview Question

Quick Overview

This question evaluates a candidate's competency in machine learning product decisions—covering target definition, dispatch-time feature design, offline evaluation metrics, online rollout guardrails, baseline establishment, and consideration of analytics-first alternatives.

Predicting Delivery ETA (Pickup → Drop-off): Case For or Against ML

Context: You’re proposing an ETA model to predict the time from rider pickup to customer drop-off for a last‑mile delivery product. Some stakeholders prefer a “pure analytics” approach. Make a structured case for or against an ML model, including guardrails and alternatives.

1) Target definition and dispatch-time features

Define the target rigorously, including how you will handle:
- Cancellations
- Rider reassignments
- Multi-stop batches (sequence position, spillover from earlier stops)
List only features available at dispatch time to avoid leakage.

2) Offline evaluation plan

Metrics (e.g., MAE, P90 error) across strata (e.g., distance, time-of-day, platform/vehicle type).
Calibration checks.
Out-of-time validation to capture seasonality.

3) Online rollout plan

Guardrail metrics (e.g., cost/order, SLA breaches).
Shadow vs. interleaved traffic strategy.
Fallback heuristics during model outage.
Fairness monitoring across zones.

4) Baseline and threshold for productionization

Define a simple-but-strong baseline (e.g., segment-aware median).
State the minimal measurable lift needed to justify engineering cost.

5) If deciding against ML

Prescribe an analytics-first alternative (e.g., policy changes, pricing/surge rules).
Provide a decision tree with triggers that revisit ML when thresholds are met.

Quick Overview

Predicting Delivery ETA (Pickup → Drop-off): Case For or Against ML

1) Target definition and dispatch-time features

Define the target rigorously, including how you will handle:
- Cancellations
- Rider reassignments
- Multi-stop batches (sequence position, spillover from earlier stops)
List only features available at dispatch time to avoid leakage.

2) Offline evaluation plan

Metrics (e.g., MAE, P90 error) across strata (e.g., distance, time-of-day, platform/vehicle type).
Calibration checks.
Out-of-time validation to capture seasonality.

3) Online rollout plan

Guardrail metrics (e.g., cost/order, SLA breaches).
Shadow vs. interleaved traffic strategy.
Fallback heuristics during model outage.
Fairness monitoring across zones.

4) Baseline and threshold for productionization

Define a simple-but-strong baseline (e.g., segment-aware median).
State the minimal measurable lift needed to justify engineering cost.

5) If deciding against ML

Prescribe an analytics-first alternative (e.g., policy changes, pricing/surge rules).
Provide a decision tree with triggers that revisit ML when thresholds are met.

Decide when to model courier ETA

Quick Overview

Predicting Delivery ETA (Pickup → Drop-off): Case For or Against ML

1) Target definition and dispatch-time features

2) Offline evaluation plan

3) Online rollout plan

4) Baseline and threshold for productionization

5) If deciding against ML

Solution

Comments (0)

Decide when to model courier ETA

Quick Overview

Predicting Delivery ETA (Pickup → Drop-off): Case For or Against ML

1) Target definition and dispatch-time features

2) Offline evaluation plan

3) Online rollout plan

4) Baseline and threshold for productionization

5) If deciding against ML

Solution

Comments (0)