📋 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