Lead Data Scientist

Middesk is building the data and intelligence infrastructure that helps businesses work together with confidence. We started by creating a comprehensive platform for understanding businesses, bringing together authoritative and proprietary data to help customers verify business identities, onboard customers faster, and manage risk throughout the customer lifecycle.

Today, Middesk is used by more than 700 banks and fintechs, and in 2025 we verified more than 7 million companies. We've also expanded beyond business verification to help companies form, register, manage, and maintain their businesses, supporting more than 50,000 companies in setting up over 100,000 accounts required to hire employees, run payroll, and stay compliant.

Middesk came out of Y Combinator, and is backed by Sequoia Capital, Accel, Insight Partners, and Canapi. We're proud to be named on the Forbes Fintech 50 and Best Startup Employers lists.

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.

We follow a hybrid work model, and for this role, there is an expectation of 2 days per week in our SF/NYC office. Candidates should be based within a commutable distance, as we believe in the value of in-person collaboration and building strong team connections while also supporting flexibility where possible.

• 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.

• 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.
  

• 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.