Data Scientist, Machine Learning

• Build risk & fraud ML applications: Deliver production ML models in fraud, trust & safety, KYB, and compliance domains, with measurable impact on customer workflows
• Tackle hard data problems: Work on classification problems with extreme class imbalance, sparse signals, and “cold start” label challenges
• Innovate in feature engineering & labeling: Use graph-based techniques, weak supervision, LLMs, and AI agents to improve signal extraction and automate labeling process
• Establish ML infrastructure foundations: Partner with platform engineering team to design feature services, model training pipeline, model serving standards, and orchestration to scale multiple ML use cases

• 7+ years applied ML experience, with direct impact in risk, fraud, trust & safety, compliance, or adjacent high-stakes domains
• Proven track record of shipping ML models from research to production in external-facing products
• Expertise in classification with real-world challenges, for example: imbalanced labels, sparse signals, cold start, and production version management
• Hands-on ML infrastructure experience: feature stores, model management, ML training/serving pipelines
• Comfort as a senior IC: setting technical direction, mentoring peers, and establishing best practices

• B2B SaaS experience, ideally building ML products for enterprise customers
• MLE/engineering collaboration experience, or direct MLE work on ML pipelines and services
• Familiarity with graph, LLM-based feature generation, or AI agent workflows
• Experience scaling ML across multiple products or risk domains

Offers Equity