Senior Data Scientist (Fraud Detection)

• We are looking for a Senior-Level Data Scientist to join our Fraud Detection team — someone equally comfortable building production ML models and getting hands-on with individual fraud cases.
• This is a dual-track role: you’ll develop the machine learning systems that catch fraud at scale, and you’ll personally lead investigations into how specific fraud attacks happened, reconstructing attacker behavior and turning case-level findings into trend reports and detection improvements.
• This is a great opportunity to grow your skills in a fast-paced, data-driven environment while making a real, visible impact in the fight against fraud
• 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
• 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

Benefits

• Stock Options
• Commuter Passes
• Paid Time Off
• Catered Lunch and Dinner
• Team and Company Outings

Preferred experience

• 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)
• Master’s or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field
• Hands-on experience with the machine learning lifecycle in a production environment
• Solid understanding of both classic machine learning models (Logistic Regression, Gradient Boosting, etc.)
• Investigator mindset: demonstrated skill in pattern synthesis, hypothesis testing, and triaging signal from noise in ambiguous, adversarial cases — not just building and monitoring models
• Excellent communication skills — able to explain complex, ambiguous, or technical behavior clearly to both technical and non-technical audiences, including customers and executives
• 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)
• Professional proficiency in written and spoken English, with the ability to collaborate effectively in a global, cross-functional team