Head of Machine Learning – Fraud & Risk

GTS Technology Solutions

Head of Machine Learning leading the Fraud & Risk ML organization for exceptional fraud detection products. Collaborating with cross-functional leaders to develop high-impact machine learning systems.

Posted 7/21/2026full-timeRemote • California • 🇺🇸 United StatesLead💰 $210,000 - $250,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading machine learning initiatives focused on fraud detection and risk management, with a strong emphasis on building and optimizing production ML models. Proven ability to mentor teams and communicate complex technical strategies to executive stakeholders.

Highest-signal resume keywords

Applied Machine LearningTeam LeadershipProduction ML Model DevelopmentPython DevelopmentFraud Detection

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Feature EngineeringModel TrainingModel EvaluationProduction DeploymentMonitoringContinuous ImprovementData PreparationModel GovernanceExperimentationTechnical Strategy

Soft Skills

LeadershipCommunicationStakeholder ManagementMentoring

Industry Keywords

Fraud DetectionFinancial RiskIdentity VerificationCybersecurity

Tech Stack

Tools & technologies

Cyber SecurityPython

About the role

Key responsibilities & impact

Lead the Fraud & Risk Machine Learning organization, managing a team responsible for production fraud detection models.
Define and execute the machine learning roadmap for fraud prevention, identity verification, and risk decisioning.
Build and scale a portfolio of production ML models from concept through deployment and continuous optimization.
Partner with Product, Engineering, Risk, and Executive Leadership to solve complex business challenges using machine learning.
Drive end-to-end machine learning development including: Feature engineering, Data preparation, Model development, Validation, Production deployment, Monitoring and model performance optimization.
Establish best practices for model governance, experimentation, and production reliability.
Mentor and grow a high-performing team of Data Scientists and Machine Learning Engineers.
Provide technical leadership while remaining capable of contributing hands-on when necessary.
Present technical strategy, business impact, and model performance to executive stakeholders.

Requirements

What you’ll need

7–15 years of experience in Applied Machine Learning or Data Science.
4+ years leading and managing Machine Learning or Data Science teams.
Proven success building and scaling production machine learning products in high-growth startup environments.
Experience leading teams responsible for ML systems that are core to the business.
Strong software engineering skills with production-level Python development.
Deep experience across the full machine learning lifecycle: Feature engineering, Model training, Model evaluation, Production deployment, Monitoring, Continuous improvement.
Domain expertise in one or more of the following: Fraud Detection, Financial Risk, Identity Verification, Cybersecurity.
Experience owning multiple production ML models rather than a single isolated project.
Strong leadership, communication, and stakeholder management skills.
Ability to communicate technical concepts clearly to executives and cross-functional partners.

Benefits

Comp & perks

Competitive equity package
Comprehensive benefits
Visa sponsorship available for qualified candidates