Position OverviewWe 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.
Key ResponsibilitiesMachine 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.
RequirementsQualificationsMaster'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: $120,000-$150,000, commensurate with experience.
- PTO, Stock Options, Health Benefits
