hawk - Data Scientist
Requirements
• 5-7 years as a data scientist in a customer-facing role, presenting analysis and defending model decisions directly to clients. This is not an internal-facing engineering or product data science background; you've sat across the table from a customer before. • Real experience in AML, fraud detection, or financial crime analytics is required. You understand transaction monitoring, typologies, and what a false positive actually costs an investigator, not just what one costs on a confusion matrix. • Strong hands-on skills in the standard data science stack (Python, SQL, and whatever ML tooling you've used in production), but your edge is judgment under ambiguity: knowing when a model change is safe to make and when it needs a human in the loop. • Comfortable being the technical voice in a room with a customer's risk, compliance, or data science stakeholders, and holding your own when questioned. • A track record of translating model performance into business value a non-technical stakeholder can act on: not just accuracy metrics, but investigator hours saved, false positive cost avoided, and cases caught. • Genuine comfort with ambiguity and live production systems. You're not looking for a clean offline research problem, you're looking for the “why did this alert fire on a real customer's data at 2pm today” problem. • An ownership mentality. You don't wait for a ticket; you notice when an account's detection performance is drifting and you go find out why. • Experience specifically in transaction monitoring or payments fraud detection at a bank, payment provider, or a vendor serving them. • Familiarity with explainable AI and rules-based hybrid detection approaches, since that's core to how Hawk's models work.
Responsibilities
• Tune and optimize detection models and thresholds against each customer's live transaction data, balancing detection effectiveness against false positive load, not just against a benchmark dataset. • Investigate detection performance deeply: dig into missed cases, alert quality, and pattern drift, and turn what you find into concrete tuning or configuration changes. • Translate technical findings into customer-facing insight: build the analysis that shows investigator productivity gains, false positive cost reduction, and detection effectiveness improvements in language a customer's compliance and risk leadership actually uses. • Sit in the room with customers directly. Present findings, defend your methodology to a customer's own data science or compliance team, and answer the hard “why did the model do this” questions live. • Partner closely with your regional Customer Value Partners on account strategy, informing where the model needs to change to unlock the next stage of value realization or a renewal conversation. • Feed patterns and findings back into Hawk's broader model and product functions, distinguishing between “this customer needs local tuning” and “this is a systemic gap worth fixing centrally.” • Own the regulatory defensibility of the tuning decisions you make. Document your reasoning so a customer's audit or regulator review holds up. • Bring rigor to how you validate model changes before they go live: backtesting, sample review, and sign-off discipline that protects the customer's compliance posture.
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