Staff Data Scientist

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Data Scientist based in the United States.

This role sits at the core of a large-scale AI-powered fraud detection platform, where data science directly protects digital trust across hundreds of global customers. You will design and own advanced machine learning systems that analyze billions of real-world events to detect and prevent sophisticated fraud and abuse patterns. The environment is highly technical and adversarial, requiring strong statistical depth and a deep understanding of security-driven data patterns. You will work closely with ML engineers, platform teams, and fraud experts to translate evolving attacker behaviors into robust, production-grade models. This is a high-impact role where your insights directly shape model architecture, feature engineering strategy, and system resilience. You will operate in a fast-moving, research-driven setting where experimentation, rigor, and production accountability are equally critical. Your work will directly reduce financial losses, improve detection accuracy, and strengthen platform trust at scale.

Accountabilities:

In this role, you will lead the design, development, and optimization of advanced machine learning models that detect fraud and abuse across massive, high-velocity datasets. You will operate at the intersection of statistics, security, and production engineering, ensuring models remain resilient against evolving adversarial behavior.

Architect and own advanced machine learning strategies for fraud detection, including payment fraud, identity abuse, account takeover, and network manipulation
Translate complex fraud and security signals into scalable modeling approaches that balance accuracy, robustness, and business impact
Design and maintain production-grade feature engineering pipelines informed by deep understanding of attacker behavior and system vulnerabilities
Establish model evaluation, monitoring, and diagnostic frameworks to detect performance degradation, data drift, and adversarial adaptation
Lead experimentation and statistical research to uncover new fraud patterns and validate signal effectiveness in production environments
Partner with ML engineers and security teams to build adversarially robust systems and ensure seamless model deployment and performance
Leverage AI tools to accelerate experimentation, automate analysis workflows, and improve modeling efficiency while maintaining statistical rigor

Requirements

This role requires deep expertise in statistical modeling, machine learning, and fraud/security domains, combined with strong production experience and the ability to operate in adversarial environments.

5+ years of hands-on data science or machine learning experience with ownership of production models at scale
Strong domain expertise in fraud, cybersecurity, or adversarial systems (e.g., payment fraud, identity abuse, account takeover, network attacks)
Advanced understanding of statistical modeling, including bias-variance tradeoffs, hypothesis testing, and model diagnostics
Experience with multiple ML paradigms including tree-based models (XGBoost, LightGBM), deep learning (CNNs, RNNs, transformers), and graph-based methods (GNNs)
Proven ability to diagnose production model failures caused by drift, adversarial adaptation, or feature leakage
Strong programming skills in Python and experience working with large-scale data environments
Ability to translate ambiguous fraud problems into structured modeling and experimentation frameworks
Experience using AI tools (LLMs, AutoML, or similar) to accelerate feature engineering and analysis while maintaining validation rigor
Advanced degree in a quantitative field (or equivalent industry experience with deep statistical modeling exposure) preferred

Benefits

Competitive compensation with performance-based incentives
Equity opportunities in a high-growth AI company
Comprehensive health, dental, and vision insurance
Flexible remote work environment across the United States
Opportunities to work on large-scale, real-world fraud and security problems
Strong learning culture with exposure to advanced ML, AI, and security domains
Collaborative environment with ML experts, engineers, and fraud specialists
Career growth in a high-impact, research-driven data science organization.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

#LI-CL1

Requirements

Benefits

USA Remote Jobs