Lyft-Specific Product Problems & Analytical Approaches Questions

Lyft-specific product challenges, problem framing, hypothesis generation, and data-driven decision making, focusing on experimentation design, metrics, and feature prioritization within the ride-hailing and on-demand transportation context. Includes product discovery, A/B testing, funnel analysis, and stakeholder alignment to improve rider and driver experiences and marketplace efficiency.

MediumTechnical

0 practiced

A 'pooling' feature was rolled out opportunistically in some neighborhoods (not randomized). You're asked to evaluate its effect on wait times and driver earnings using observational data. Propose an identification strategy, list the assumptions required, describe methods to adjust for confounding (matching, DID, synthetic control), and recommend sensitivity analyses and diagnostics to build confidence in causal claims.

HardSystem Design

0 practiced

Design an agent-based simulation to estimate city-level effects of a new optimized pooling algorithm on traffic congestion, driver earnings, rider wait times, and CO2 emissions. Define agent behaviors, environment, key parameters to calibrate from historical data, metrics to collect, sensitivity analyses to run, and how you'd validate the simulator before using it to recommend policy.

HardTechnical

0 practiced

Describe an approach to estimate 3-year rider lifetime value (LTV) combining a frequency model (e.g., Pareto/NBD or survival model) and a monetary model. Include feature engineering, handling right-censoring, time-varying covariates (promotions, seasonality), evaluation metrics, and how you'd validate and calibrate LTV forecasts for decision making (marketing budgets, acquisition bids).

MediumTechnical

0 practiced

Given these tables:

drivers_status(driver_id, zone_id, status varchar, updated_at timestamp)
ride_requests(request_id, zone_id, requested_at timestamp, matched_at timestamp null)

Write an ANSI-SQL query that, for each zone and hour in the last 30 days, computes: idle_driver_count (status='idle' as of hour), unmatched_requests (requests with no matched_at within 5 minutes), and imbalance = unmatched_requests - idle_driver_count. Return zone_id, hour_start, idle_driver_count, unmatched_requests, imbalance.

EasyTechnical

0 practiced

You have the following table schema:

rides(
  ride_id varchar primary key,
  city varchar,
  rider_id varchar,
  driver_id varchar null,
  requested_at timestamp,
  matched_at timestamp null,
  pickup_at timestamp null,
  dropoff_at timestamp null,
  cancelled boolean,
  cancel_reason varchar
)

Write a single ANSI SQL query that returns weekly funnel counts and conversion rates per city for: requests, matched, pickups, dropoffs. Columns should include: week_start, city, requests, matched, pickups, dropoffs, req_to_match_pct, match_to_pickup_pct, pickup_to_dropoff_pct. Ensure you handle duplicates and nulls.

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