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# Customer Data Scientist

- **Company:** Hawk, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $150,000.0 - $225,000.0
- **Contract:** Permanent contract
- **Skills:** Customer Data Management, Fraud Prevention and Detection, Python (Programming Language), SQL Databases, Transaction Data, Explainable AI (XAI)
- **Published:** October 20, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/psza6lcxtq

## About the Role

* 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., * Familiarity with explainable AI and rules-based hybrid detection approaches, since that's core to how Hawk's models work.

## Description

* 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., * Experience specifically in transaction monitoring or payments fraud detection at a bank, payment provider, or a vendor serving them.