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About the Role
As a Customer Data Scientist at Hawk, you will be the trusted expert for customers, ensuring their AML and fraud detection models are effectively tuned to their specific transaction patterns, defensible to regulators, and demonstrably effective. You will work directly within customer-facing regional teams, collaborating with Customer Value Partners on live accounts to tune models, conduct in-depth analytical investigations, and communicate improvements to customers.
Responsibilities
- Tune and optimize detection models and thresholds against live customer transaction data, balancing detection effectiveness against false positive load.
- Investigate detection performance, including missed cases and alert quality, and implement concrete tuning or configuration changes.
- Translate technical findings into customer-facing insights on investigator productivity gains, false positive cost reduction, and detection effectiveness improvements.
- Present findings and defend methodology directly to customers, including their data science or compliance teams.
- Partner with regional Customer Value Partners on account strategy, informing model changes for value realization.
- Feed patterns and findings back into Hawk's broader model and product functions, distinguishing between customer-specific tuning and systemic issues.
- Own the regulatory defensibility of tuning decisions, documenting reasoning for audit and regulator reviews.
- Validate model changes through backtesting, sample review, and sign-off discipline before deployment.
Requirements
- 5-7 years of experience as a data scientist in a customer-facing role, with a proven ability to present analysis and defend model decisions directly to clients.
- Direct experience in AML, fraud detection, or financial crime analytics, including understanding transaction monitoring, typologies, and the cost of false positives.
- Strong hands-on skills in Python, SQL, and production ML tooling.
- Demonstrated judgment under ambiguity, knowing when model changes are safe and when human intervention is needed.
- Comfortable acting as the technical voice in meetings with customer risk, compliance, or data science stakeholders.
- Track record of translating model performance into actionable business value for non-technical stakeholders, such as investigator hours saved or false positive costs avoided.
- Genuine comfort with ambiguity and live production systems.
- An ownership mentality, proactively identifying and investigating performance drifts.
Skills
- Python
- SQL
- Machine Learning
- AML
- Fraud Detection
- Financial Crime Analytics
- Transaction Monitoring
- Explainable AI
- Rules-based Systems
Location
- Remote
Work Type
- Full-time
Experience Level
- Mid-level
- Senior
About the Company
- Hawk is a leading provider of AI-supported anti-money laundering and fraud detection technology.
- Banks and payment providers globally use Hawk's solution to improve AML compliance and fraud prevention effectiveness.
- The company's technology combines traditional rules with explainable AI to identify more crime and reduce false positives.
- Hawk plays a vital role in combating Money Laundering, Fraud, and the financing of terrorism.
- The company offers a culture of mutual trust, support, and passion, with opportunities for professional growth and making a difference.