Detect and quantify wash trading
Company: Coinbase
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: hard
Interview Round: Technical Screen
Define an analytics approach to detect and quantify wash trading on BTC‑USD and thin‑liquidity altcoin pairs on a centralized exchange. Detail: a) features from order/trade logs (self‑matches, rapid round‑trips, size/price mirroring across linked accounts, order‑book position churn), b) graph heuristics to infer common control (shared devices/IPs, on‑chain funding links) with privacy‑preserving hashing and strict access controls, c) thresholds that separate legitimate market making from manipulation (include precision/recall trade‑offs), d) backtesting using synthetic injected wash trades plus any available enforcement ground truth, e) a daily risk score with confidence intervals and calibration checks, and f) how to avoid penalizing bona fide liquidity providers and how you would surface cases to Compliance for review.
Overview: This question evaluates a candidate's skills in fraud detection analytics, feature engineering from order and trade logs, graph-based identity inference, model calibration and backtesting for market-manipulation detection.