Opendoor - Data Science - Sharing interview question - AMA
Just helping the community and sharing the question I received during my 1hr interview for Data Science at Opendoor.
Outcome: reject, so AMA #opendoor
----
At Opendoor, we care a lot about ensuring high valuation accuracy. We have a combination of human and algorithmic valuators who -- in both training scenarios and in production -- produce numerous valuations for different homes.
Given a manually-entered dataset of multiple valuators’ valuations (keyed by valuator_id) of different homes (keyed by home_id), evaluated against their ultimate close_prices.
We wish to understand the performance of our valuators. Define and calculate some metrics that will help us understand valuator performance. [max 10m]
How similar are our valuators’ estimates to one another? What implications does this have for reducing error via combining estimates? [max 15m]
Create and evaluate a rule that will combine valuations to achieve higher accuracy than any individual valuator. [max 15m]
Considering operational and cost constraints, design a system that will help us trade off among our key business objectives. [remaining time]
Sample data below:
home_id close_price valuator_id valuation
1 220674. V1 222988.
1 220674. V2 226617.
1 220674. V3 229378.
1 220674. V4 214982.
1 220674. V5 224865.
2 251957. V1 248071.
comments