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
As a Data Scientist at Hawk, you’ll play a key role in enhancing our AI-driven Anti-Financial Crime platform, specifically targeting the detection of money laundering and fraud. You will leverage cutting-edge machine learning models and anomaly detection techniques to develop scalable solutions that make a tangible difference in preventing financial crime. Working hands-on with data, you’ll ensure that our platform remains at the forefront of fraud prevention and compliance, contributing to a safer financial ecosystem.
Your Responsibilities
Design, build, and deploy machine learning models for detecting money laundering and fraud.
Analyze large datasets to uncover trends, anomalies, and actionable insights.
Implement scalable data pipelines for model training and deployment.
Stay up to date on AI and ML innovations to continuously improve platform performance.
Collaborate with cross-functional teams to develop data-driven strategies.
Your Profile
Ideally 3+ years of experience as a Data Scientist.
Experience with deep learning (TensorFlow, PyTorch) and tree-based models (e.g., XGBoost).
Expertise in anomaly detection techniques and familiarity with NLP models (e.g., transformers) is a plus.
Proficiency in Python and machine learning libraries.
Strong communication skills and a passion for solving data-driven challenges.
MSc or PhD in Computer Science, Mathematics, Statistics, or related field.