Lead Data Scientist

Join Middesk, a company focused on building AI-driven applications that streamline customer workflows. As a Lead Data Scientist, you will be responsible for delivering production ML models in fraud, trust & safety, KYB, and compliance domains. You will tackle hard data problems, innovate in feature engineering & labeling, establish ML infrastructure foundations, and design and implement knowledge graph solutions. This is a highly technical, hands-on role with wide influence on how we design, build, and scale ML at Middesk.

• Lead the development and implementation of machine learning models for risk and fraud applications, ensuring measurable impact on customer workflows.
• Collaborate with cross-functional teams to tackle complex data problems, innovate in feature engineering, and establish ML infrastructure foundations.
• Mentor and guide peers in best practices for machine learning, setting technical direction, and establishing standards for model training and serving.
• Comfort as a senior IC: setting technical direction, mentoring peers, and establishing best practices
• 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
• Expertise in classification with real-world ML challenges, for example: imbalanced labels, sparse signals, cold start, and production version management
• 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
• 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
• Hands-on ML infrastructure experience: feature stores, model management, ML training/serving pipelines
• 5+ years of production ML experience in one or more of the following areas:
• 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