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.
Will definitely answer this after work OP
Can anybody help with the answer for this ?
Was this more high level design question or coding was also involved?
Working Parents
20h
1394
Closed now - thank you all
Tech Industry
9h
1027
Women, help me understand why this is inspirational
India
Yesterday
1303
Modi is a legend, will be remembered for centuries to come
Personal Finance
2d
2903
Is spending 12K per month normal or too much
Tech Industry
2h
1591
What happens when most of your team is Indian?
What's the q
updated in initial post