Data Scientist

About the role

We are seeking a hands-on engineer to help build the foundation for our AI-driven applications that simplify customer workflows, starting with business onboarding. This role involves applying the right techniques to messy, real-world problems in fraud, risk, and trust domains.

Responsibilities

• Build fraud & risk systems
• Design and ship production systems that detect and prevent fraud across KYB, trust & safety, and compliance workflows
• Work with messy, real-world data
• Tackle problems with extreme class imbalance, sparse signals, evolving adversarial behavior, and limited ground truth
• Leverage relationships in data
• Apply graph-based approaches and entity resolution techniques to uncover hidden connections and improve risk detection
• Improve signal & labeling
• Use a mix of heuristics, weak supervision, and modern AI tools (including LLMs where appropriate) to generate better features and labels
• Help scale our infrastructure
• Partner with engineering to build and evolve systems for feature generation, model training, and production deployment across multiple use cases

Requirements

• 5+ years of experience in fraud, risk, or trust & safety
• You've worked on real-world fraud or abuse problems and understand the domain deeply
• Experience building and shipping production systems
• You’ve deployed models or data-driven systems that power external-facing products
• Strong foundation in applied ML or data systems
• Comfortable working on classification problems with real-world constraints like imbalanced data, sparse signals, and changing patterns
• Experience with graph or relational data approaches
• Familiarity with knowledge graphs, network analysis, or entity linking is strongly preferred
• Hands-on and pragmatic
• You focus on impact over perfection and know how to balance speed, accuracy, and maintainability