Staff Data Scientist Fraud and Risk

You will serve as a Staff Data Scientist in Fraud and Risk, building and improving classical machine-learning models for global fraud detection and risk mitigation. You will develop production GenAI and agentic workflows, manage drift and imbalanced data, guide technical standards, and partner with engineering, product and business stakeholders.

Responsibilities

• Design and develop predictive machine-learning models for fraud detection, risk assessment and anomaly detection
• Design and develop production-grade GenAI and agentic AI solutions
• Handle highly imbalanced datasets
• Identify, measure and resolve data drift and concept drift
• Present insights and translate model outputs for non-technical stakeholders
• Set technical standards and review architectures
• Collaborate with ML engineers to deploy models
• Establish CI/CD pipelines and implement model tracking and observability
• Partner with Data Engineering to optimize feature engineering and feature stores
• Translate fraud typologies and business requirements into data-science problems
• Build and track model performance metrics and business impact
• Propose innovative solutions in ambiguous situations

Requirements

• 10+ years of experience in Data Science or Machine Learning
• Substantial experience fighting fraud or mitigating risk
• Deep theoretical and practical understanding of classical machine-learning algorithms
• Experience with XGBoost, LightGBM, Random Forests, SVMs and ensemble methods
• Experience building adversarial fraud-detection models
• Experience with synthetic data generation
• Production experience with GenAI and multi-agent systems
• Experience with LangGraph, AWS Bedrock or the GCP ecosystem
• Experience handling highly imbalanced datasets
• Knowledge of sampling methods, cost-sensitive learning and evaluation metrics
• Experience detecting and addressing data drift, concept drift and model degradation
• Strong SQL knowledge
• Strong Python programming skills
• Exposure to PyTorch and Hugging Face
• Knowledge of ML and data-science inferencing performance optimization
• Understanding of Spark and Hadoop
• Strong knowledge of AWS or GCP
• Experience with Databricks, Snowflake, BigQuery, Tecton or Fiddler

Benefits

• Competitive salary and share options
• Generous annual leave
• Paid maternity, paternity and adoption leave
• Sabbatical leave options
• Private family health insurance
• Therapy sessions
• Courses
• Meditations
• Workshops
• Paid volunteering and development days
• Annual learning and development budget
• Work from abroad for up to 90 days annually
• Home office setup contribution
• Laptop replacement benefit
• Office snacks, coffee, tea and lunch