Lead Data Scientist, Middesk

• 5+ 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

Desirable

• We are actively building AI-driven applications that streamline customer workflows, focusing on business onboarding. With our proprietary identity data assets and deep domain expertise, we are uniquely positioned to expand into a broader set of AI-powered solutions that drive long-term growth
• We’re looking for a hands-on applied ML expert to help build the technical foundation for these efforts. Ideally you have shipped external-facing models in the risk/fraud space and know the messy realities of imbalanced data, low labels, and changing behavior. This is a highly technical, hands-on role with wide influence on how we design, build, and scale ML at Middesk
• 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