Lead Data Scientist

Middesk

Lead Data Scientist creating risk and fraud ML applications at Middesk to improve customer workflows. Focus on building foundational ML infrastructure with a hybrid work model.

Posted 5/27/2026full-timeSan Francisco • California • 🇺🇸 United StatesSenior💰 $210,000 - $250,000 per yearWebsite

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

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Hard Skills & Tools

machine learningfeature engineeringclassificationknowledge graphsignal extractionmodel training pipelinemodel servingentity resolutionweak supervisiongraph-based techniques

Soft Skills

mentoringtechnical directionbest practices establishment