Staff Data Scientist, Gemini

• ​​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

Desirable

• At Gemini, our Data Team is the engine that powers insight, innovation, and trust across the company. We bring together world-class data engineers, platform engineers, machine learning engineers, analytics engineers, and data scientists — all working in harmony to transform raw information into secure, reliable, and actionable intelligence.
• From building scalable pipelines and platforms, to enabling cutting-edge machine learning, to ensuring governance and cost efficiency, we deliver the foundation for smarter decisions and breakthrough products.
• We thrive at the intersection of crypto, technology, and finance, and we’re united by a shared mission: to unlock the full potential of Gemini’s data to drive growth, efficiency, and customer impact
• As a Staff Data Scientist focused on Machine Learning for Risk, you’ll play a key role in protecting our customers and platform.
• You’ll work cross-functionally with product, engineering, and operations to design and deploy models that detect, prevent, and mitigate fraud risk across Gemini’s ecosystem.
• You’ll own the full machine learning lifecycle from identifying fraud signals and engineering features to training, evaluating, and deploying models in production.
• You’ll partner with stakeholders across Trust & Safety, Exchange Growth, and Credit Card to improve risk scoring, detect new fraud patterns, and enhance our ability to distinguish bad actors from trusted customers. This is a high-impact, hands-on individual contributor role with opportunities for technical leadership and mentorship
• This role is required to be in person twice a week at either our San Francisco, CA or New York City, NY office
• 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