Associate Data Scientist

Responsibilities

• Build and maintain features and components for graph-native models (GNNs, embeddings, graph algorithms) used to detect fraud, collusion, and identity risk
• Support the design, training, and evaluation of statistical, AI/ML, and deep learning models for fraud and risk, under a senior data scientist's guidance
• Run experiments, clean and prepare data, and do light pipeline work to help tune model performance
• Write production-quality code backed by statistics and ML, and help build out ML observability for the models you work on
• Document your work clearly enough that others on the team can pick it up

Requirements

• BS/MS in ML, CS, Math, Stats, or a related field; 1 to 3 years in DS/AI/ML Engineering roles (internships with real shipped work count)
• Strong Python; working knowledge of SQL; exposure to a cloud platform (AWS/Databricks/Azure) is a plus
• Some experience taking a model from a notebook toward something production-adjacent, ideally with fraud, risk, payments, or compliance data — but we'll take strong fundamentals over domain experience
• Comfort with ambiguity and unlabelled data, and the initiative to ask questions and look things up rather than wait to be told
• Prior exposure to graph-based thinking (even coursework or personal projects) is a plus, not a requirement

Our Culture

• We hire self-motivated people and get out of their way
• We value performance, not hours worked
• Speed, ownership, and impact matter most

Competitive salary + potential equity

Health benefits, flexible PTO, learning budget