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# Lead Data Scientist

- **Company:** Middesk
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Software as a Service, Data Validation, Fraud Prevention and Detection, Graph Database, Intrusion Detection Systems, Machine Learning, Feature Engineering, Large Language Models, Software Security, Machine Learning Operations, Virtual Agents, Software Version Control
- **Published:** July 31, 2026
- **Apply:** https://www.careerbuilder.com/job-details/lead-data-scientist-san-francisco-ca--4336bf74-2473-4844-ac7e-2177a9bb8263

## About the Role

* 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.

Skills:
Applications Security, Artificial Intelligence (AI), Artificial Intelligence (AI) Agents, Automation Engineering, Best Practices, Business Case, Business-to-Business (B2B), Construction, Data Modeling, Data Quality, Data Science, Integrated Circuits (ICs), Know Your Customer (KYC), Mentoring, Onboarding, Product Development, Risk, Risk Management, Software as a Service (SaaS), Team Building, Team Lead/Manager, Use Cases

## Description

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.
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.


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