Staff Data Scientist
New
Based in the United StatesFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonCybersecurityMachine LearningDeep Learning
Requirements
- 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
Responsibilities
- 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
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