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.
Location: US Remote
Your Mission
Hawk AI is seeking a hands-on Lead Data Scientist to help shape the future of AI-driven financial crime detection. You will work closely with the Chief Data Scientist, focusing on customer-facing AI solutions. You will be responsible for developing, deploying, and optimizing machine learning models that enhance our Anti-Financial Crime platform, ensuring they are effective, scalable, and built with US market expectations in mind. This is a player-coach role, ideal for a senior data scientist who is both hands-on and capable of guiding junior team members.
Your Responsibilities
Develop and deploy machine learning models focused on anomaly detection to detect money laundering and fraudulent activity.
Enhance and optimize data pipelines, integrating customer feedback to improve detection accuracy.
Lead AI model governance and testing, ensuring models are interpretable, scalable, and reliable.
Work closely with customers and stakeholders, translating feedback into model improvements and product enhancements.
Build and deploy AI solutions, leveraging MLOps best practices for production-ready implementations.
Collaborate with cross-functional teams (engineering, product, and business) to integrate AI solutions into Hawk AI’s offerings.
Stay ahead of AI trends and financial crime strategies, developing expertise in financial crime detection methodologies.
Mentor junior team members, providing technical guidance while remaining hands-on with development.
Your Profile
5+ years of experience in AI, machine learning, and data science, with hands-on deployment experience.
Proficiency in Python and machine learning frameworks (PyTorch, TensorFlow, Keras).
Strong experience with Kubernetes and Docker for containerized AI deployments.
Cloud experience (AWS preferred, but not required), with knowledge of cloud-based AI model deployment.
Expertise in SQL and standard data manipulation techniques.
Anomaly detection experience—a plus if it's in relation to detecting financial crime patterns.
MLOps experience, including model monitoring, retraining strategies, and production pipelines.
Experience with AI testing platforms (e.g., MLflow) is a plus, as well as C++ experience.
Full-stack mindset—ability to build, deploy, and refine AI solutions in production.
Strong customer interaction skills—ability to understand customer needs and translate them into technical solutions.
Structured thinker, capable of both technical innovation and hands-on implementation.
Ph.D. or Master’s in Computer Science, Mathematics, Statistics, or a related field preferred but not required.
What We’re Looking For
An end-to-end data scientist, comfortable with both model development and deployment.
A strategic thinker who can identify patterns and devise effective countermeasures.
A player-coach—hands-on and willing to guide others, but also able to lead technical initiatives.
Someone curious and eager to learn about the world of financial crime.
This role is a unique opportunity to work at the intersection of AI and financial crime prevention, developing cutting-edge solutions that make a real-world impact.