How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

What difficulty level is this interview question?

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

What role is this question designed for?

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

Design Dynamic Pricing System for Lyft: Key Features & Models

Quick Overview

This question evaluates a candidate's ability in dynamic pricing and real-time machine learning system design, focusing on demand/supply forecasting, price elasticity modeling, multi-objective optimization, fairness and safety constraints, feature engineering from streaming and historical data, and online experimentation.

Scenario

You are designing Lyft's real-time dynamic-pricing system to jointly optimize rider experience and marketplace health. The system should adjust prices at fine spatial and temporal resolution while accounting for demand spikes, driver availability, and regulatory/fairness constraints.

Key outcomes to balance:

Rider ETA (wait time)
Rider conversion (request and completion probability)
Driver earnings and utilization
Company revenue/GMV

Task

Design a dynamic pricing algorithm. Describe:

Features and data sources you would use in real time and historically.
The modeling components and overall architecture (e.g., forecasting, elasticity, policy/optimization; tree models vs. RL/bandits).
How you would balance trade-offs across objectives, including surge caps, fairness constraints, and safety guardrails.
How you would evaluate and monitor the system online.

Notes

Consider real-time demand/supply, location grid, time-of-day, historical price elasticity, driver proximity/ETA, weather/traffic, and special events.
Discuss model choices such as gradient-boosted trees vs. RL/bandits, and when to use each.
Include guardrails (e.g., surge caps, rate limits, regional fairness) and an experimentation/safety plan.

Quick Overview

Scenario

Key outcomes to balance:

Rider ETA (wait time)

Rider conversion (request and completion probability)

Driver earnings and utilization

Company revenue/GMV

Task

Design a dynamic pricing algorithm. Describe:

Features and data sources you would use in real time and historically.

The modeling components and overall architecture (e.g., forecasting, elasticity, policy/optimization; tree models vs. RL/bandits).

How you would balance trade-offs across objectives, including surge caps, fairness constraints, and safety guardrails.

How you would evaluate and monitor the system online.

Notes

Consider real-time demand/supply, location grid, time-of-day, historical price elasticity, driver proximity/ETA, weather/traffic, and special events.

Discuss model choices such as gradient-boosted trees vs. RL/bandits, and when to use each.

Include guardrails (e.g., surge caps, rate limits, regional fairness) and an experimentation/safety plan.

Design Dynamic Pricing System for Lyft: Key Features & Models

Quick Overview

Scenario

Task

Notes

Solution

Submit Your Answer

Design Dynamic Pricing System for Lyft: Key Features & Models

Quick Overview

Scenario

Task

Notes

Solution

Submit Your Answer