Senior Data Scientist - Fraud Detection

Position Overview

Key Responsibilities

• Machine Learning & Model Development
• End-to-End Model Development: Lead the full lifecycle of fraud detection features and models, from ideation and data exploration to prototyping, productionizing, and monitoring.
• Advanced Feature Engineering: Develop highly predictive features from complex, large-scale, multi-dimensional data, including user behavior, device intelligence, network graphs, and transaction records.
• Large-Scale Data Processing: Work with massive, noisy, and imbalanced datasets (billions of events) using tools like Spark, SQL, and our proprietary AI platform.
• Agentic AI & Automation: Leverage agentic AI to automate analytic pipelines and develop reusable skill tools that accelerate fraud investigation, feature generation, and reporting workflows.
• Live Fraud Investigation & Reporting
• Lead investigations into complex fraud cases across identities, accounts, devices, and transaction surfaces. Reconstruct attacker sequences and hypothesize actor intent and tooling.
• Produce clear, evidence-backed technical reports and case studies for product, engineering, operations, legal, and executive stakeholders.
• Generate fraud trend reports for customers, synthesizing case-level findings and aggregate data into narratives customers can act on.

Requirements

• Master's or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.
• 3+ years of applied experience in fraud detection, cybersecurity, or a related adversarial/high-velocity risk domain (fintech, consumer payments, banking, SaaS, marketplace risk, or security research).
• Solid understanding of both classic machine learning models (Logistic Regression, Gradient Boosting, etc.).
• Hands-on experience with the machine learning lifecycle in a production environment.
• Investigator mindset: demonstrated skill in pattern synthesis, hypothesis testing, and triaging signal from noise in ambiguous, adversarial cases — not just building and monitoring models.
• Strong programming skills in Python (must-have) and proficiency with SQL; experience with PySpark is a significant plus.
• Experience with large-scale data tools (Spark, Hadoop, etc.) and cloud platforms (AWS, GCP, Azure).
• Excellent communication skills — able to explain complex, ambiguous, or technical behavior clearly to both technical and non-technical audiences, including customers and executives.
• Professional proficiency in written and spoken English, with the ability to collaborate effectively in a global, cross-functional team.

Benefits

• Base salary range: $140,000–$170,000, commensurate with experience.
• PTO, Stock Options, Health Benefits