Customer Data Scientist

📋 Description Tune and optimize detection models and thresholds against each customer's live transaction data Investigate detection performance deeply: dig into missed cases, alert quality, and pattern drift Translate technical findings into customer-facing insight: build the analysis that shows Sit in the room with customers directly. Present findings, defend your methodology to a customer's Partner closely with your regional Customer Value Partners on account strategy, informing where the Feed patterns and findings back into Hawk's broader model and product functions, distinguishing 🎯 Requirements 5-7 years as a data scientist in a customer-facing role, presenting analysis and defending model Real experience in AML, fraud detection, or financial crime analytics is required. You understand Strong hands-on skills in the standard data science stack (Python, SQL, and whatever ML tooling Comfortable being the technical voice in a room with a customer's risk, compliance, or data science A track record of translating model performance into business value a non-technical stakeholder can Genuine comfort with ambiguity and live production systems. You're not looking for a clean offline 🎁 Benefits Experience specifically in transaction monitoring or payments fraud detection at a bank, payment Familiarity with explainable AI and rules-based hybrid detection approaches, since that's core to