Machine Learning Lead

OVERVIEW
Our client is seeking a highly skilled Machine Learning Lead to build and advance the fraud and risk intelligence capabilities at the core of its payment platform. This is a foundational leadership opportunity to establish machine learning strategy, develop scalable fraud detection systems, and create intelligent solutions that improve approval rates, reduce fraud, and strengthen merchant risk management.
The ideal candidate combines deep expertise in machine learning with hands-on experience in payments fraud, acquiring, and risk decisioning. This individual will lead the development of production-grade models, partner across teams, and help shape the long-term roadmap for fraud prevention, underwriting, and intelligent payment optimization.

QUALIFICATIONS
Required
Bachelor’s degree in Computer Science, Data Science, Statistics, Engineering, Mathematics, or a related field; advanced degree preferred.
Five or more years of experience in machine learning, applied data science, or production ML environments.
Direct experience building fraud and risk models within payments, acquiring, payment service providers, payment facilitators, or related financial technology environments.
Demonstrated success deploying machine learning models into production and managing the full model lifecycle.
Strong expertise in machine learning methodologies, statistics, feature engineering, and analysis of high-volume transaction data.
Deep understanding of authorization fraud, card-not-present fraud, chargebacks, merchant risk, and payment ecosystem dynamics.
Experience collaborating across technical and business teams in fast-paced environments.

Preferred
Experience working for an acquirer, ISO, PayFac, payments processor, or payments infrastructure company.
Experience designing and scaling MLOps frameworks, monitoring systems, and automated retraining processes.
Familiarity with cloud computing environments supporting large-scale machine learning workloads.
Knowledge of card network rules, disputes, chargeback workflows, and fraud liability frameworks.
Experience as an early-stage or founding machine learning leader within a startup.
Exposure to real-time fraud scoring systems, stablecoins, cryptocurrency, or alternative payment technologies.

Key Competencies & Attributes

Strong analytical and problem-solving skills.

Strategic mindset with the ability to balance innovation and execution.

Ability to work effectively in ambiguous and rapidly evolving environments.

Excellent communication and collaboration skills.

Entrepreneurial approach with a bias toward action.

High attention to detail and commitment to measurable outcomes.

Passion for applying advanced technology to solve complex business challenges.

KEY RESPONSIBILITIES
Fraud Detection & Risk Modeling
Design, develop, and deploy machine learning models that strengthen fraud detection and risk decisioning capabilities.
Engineer and evaluate features using transaction, behavioral, and external data sources.
Continuously improve model performance through experimentation, monitoring, retraining, and optimization.
Identify emerging fraud patterns and create scalable approaches to mitigate evolving threats.

Model Development & Operations
Own the full machine learning lifecycle, from proof-of-concept through production deployment.
Establish model governance, monitoring processes, and performance reporting frameworks.
Define and track key metrics including fraud detection rates, false positives, chargeback ratios, approval rates, and dispute outcomes.
Support the design and scaling of MLOps infrastructure and cloud-based ML environments.

Cross-Functional Collaboration
Partner with Engineering, Product, Operations, and Risk teams to embed fraud intelligence directly into payment workflows and internal systems.
Evaluate and integrate third-party fraud and risk solutions to maximize performance and efficiency.
Translate complex analytical findings into actionable business insights and recommendations.

Strategic Leadership
Establish foundational machine learning and data science practices across the organization.
Contribute to long-term fraud, risk, and AI strategies aligned with business growth.
Build frameworks, tools, and processes that support future team expansion and organizational scale.