Customer Data Scientist

About the role

About Us

Hawk is the leading provider of AI-supported anti-money laundering and fraud detection technology. Banks and payment providers globally are using Hawk’s powerful combination of traditional rules and explainable AI to improve the effectiveness of their AML compliance and fraud prevention by identifying more crime while maximizing efficiency by reducing false positives. With our solution, we are playing a vital role in the global fight against Money Laundering, Fraud, or the financing of terrorism. We offer a culture of mutual trust, support and passion – while providing individuals with opportunities to grow professionally and make a difference in the world.

YOUR MISSION https://hawkai.atlassian.net/browse/LGL-1378?atlOrigin=eyJpIjoiMmE1MGVmM2FmM2UxNDNjMDgxNTBiMzhiZjIzOWY1YjgiLCJwIjoiaiJ9

As a Customer Data Scientist at Hawk, you're the person our customers trust to make their AML and fraud detection models actually work for them: tuned to their transaction patterns, defensible to their regulators, and provably effective in their own numbers. You sit inside the regional customer team, working directly alongside Customer Value Partners on live accounts, not behind a wall of tickets from a central data science function. Your work spans model and threshold tuning, deep analytical investigation into detection performance, and building the customer-facing narrative that shows exactly what's improved and why. You've done this in front of customers before, and you know the difference between a model that scores well in a notebook and one that survives contact with a real investigator's workload. https://hawkai.atlassian.net/browse/LGL-1378?atlOrigin=eyJpIjoiMmE1MGVmM2FmM2UxNDNjMDgxNTBiMzhiZjIzOWY1YjgiLCJwIjoiaiJ9

KEY RESPONSIBILITIES https://hawkai.atlassian.net/browse/LGL-1378?atlOrigin=eyJpIjoiMmE1MGVmM2FmM2UxNDNjMDgxNTBiMzhiZjIzOWY1YjgiLCJwIjoiaiJ9

- 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. https://hawkai.atlassian.net/browse/LGL-1378?atlOrigin=eyJpIjoiMmE1MGVmM2FmM2UxNDNjMDgxNTBiMzhiZjIzOWY1YjgiLCJwIjoiaiJ9

- Investigate detection performance deeply: dig into missed cases, alert quality, and pattern drift, and turn what you find into concrete tuning or configuration changes. https://hawkai.atlassian.net/browse/LGL-1378?atlOrigin=eyJpIjoiMmE1MGVmM2FmM2UxNDNjMDgxNTBiMzhiZjIzOWY1YjgiLCJwIjoiaiJ9

- 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. https://hawkai.atlassian.net/browse/LGL-1378?atlOrigin=eyJpIjoiMmE1MGVmM2FmM2UxNDNjMDgxNTBiMzhiZjIzOWY1YjgiLCJwIjoiaiJ9

- 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. https://hawkai.atlassian.net/browse/LGL-1378?atlOrigin=eyJpIjoiMmE1MGVmM2FmM2UxNDNjMDgxNTBiMzhiZjIzOWY1YjgiLCJwIjoiaiJ9