Design, build, and deploy machine learning models for real-time fraud detection and risk scoring.
Automate the monitoring of fraud and risk metrics using AI-driven alert systems.
Develop automated agents and systems to investigate and respond to suspicious activity, minimizing manual intervention.
Lead the research and implementation of cutting-edge AI and machine learning techniques for fraud prevention.
Conduct deep analysis of complex fraud patterns using advanced statistical and machine learning techniques.
Collaborate with engineering and product teams to integrate fraud prevention logic directly into our products.
Continuously optimize our models and detection strategies based on performance data.
Own the development and reporting of KPIs that measure the effectiveness of our AI-based fraud systems.

Proven experience building and deploying machine learning models in a cloud environment (GCP preferred).
Strong command of Python and SQL (BigQuery), not just for querying but for creating data pipelines and models.
Hands-on experience with machine learning libraries and frameworks (e.g., Scikit-learn, TensorFlow, PyTorch).
Familiarity with data workflow and orchestration tools (e.g., Airflow) is a major plus.
A deep understanding of anomaly detection, fraud, and risk modeling techniques.
A proactive and creative mindset with a passion for using technology to solve complex challenges.
Excellent communication skills and the ability to translate technical findings into business strategy.
Eagerness to take ownership of projects and lead the transition to an "AI-first" culture.

Data Scientist – Fraud Prevention

Key skills