Lead Data Scientist

What You'll Do

• 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 the ML infra team to design feature services, model training pipeline, model serving standards, and orchestration to scale multiple ML use cases.
• Design and implement knowledge graph solutions: Leveraging LLMs for graph construction, querying, and retrieval to enhance entity resolution and business identity use cases.

What We're Looking For

• 7+ years of production ML experience in one or more of the following areas:
• Building Production ML for risk, fraud, credit, or trust & safety: Track record of shipping external-facing ML applications in one or more of these domains.
• Knowledge graph applications: Hands-on experience building, querying, or extracting signals from knowledge graphs—ideally over business entity networks (companies, persons, addresses, relationships) to support identity verification, fraud detection, or risk decisioning.
• Entity resolution for business or individual identities: Experience disambiguating and linking records across noisy, incomplete, or conflicting data sources—particularly in KYB, KYC, AML, or identity verification contexts where the same real-world entity may appear under different names, addresses, or tax IDs.
• Expertise in classification with real-world ML 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.

Nice-To Have

• B2B SaaS experience, ideally building ML products for enterprise customers.
• ML pipeline and automation engineering: Experience building end-to-end training harnesses that automate feature engineering, data validation, and model training.
• Experience scaling ML across multiple products or risk domains.