Responsibilities:

📍Analyze large, complex datasets to identify key fraud indicators and engineer predictive features using internal and external data sources. 📍Design, train, and deploy machine learning models to identify and prevent fraud, including payment fraud, account takeovers, and identity abuse. 📍Build and maintain end-to-end data and model pipelines for risk scoring, anomaly detection, and behavioral profiling. 📍Evaluate model performance through experiments, backtesting, and continuous monitoring to improve capture rates and reduce false positives. 📍Partner with product managers, engineers, and fraud operations to translate model outputs into effective prevention strategies and user-facing features. 📍Communicate findings and recommendations to technical and non-technical audiences, influencing strategy and prioritization. 📍Stay current on emerging fraud tactics and machine learning approaches to continually evolve Gemini’s defenses.

Minimum Qualifications:

📍​​Bachelor’s degree in Computer Science, Data Science, Statistics, or a related field. 📍8+ years of experience (5+ years with PhD) applying data science and machine learning to financial, payments, or fraud-related problems. 📍3+ years of experience developing, deploying, and maintaining production-grade ML models, ideally for real-time or large-scale applications. 📍Strong proficiency in Python and relevant modeling libraries (eg, scikit-learn, xgboost, TensorFlow, PyTorch) and SQL. 📍Experience with data processing and model lifecycle tools such as Databricks, SageMaker, Snowflake, MLflow, or similar. 📍Familiarity with orchestration and data pipeline frameworks (e.g., Airflow, Spark). 📍Demonstrated ability to work cross-functionally with product, engineering, and operations teams. 📍Excellent communication skills and the ability to translate complex technical concepts into actionable insights.

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