Role overview
About the Company
Gemini is a global crypto and Web3 platform founded by Cameron and Tyler Winklevoss in 2014, offering a wide range of simple, reliable, and secure crypto products and services to individuals and institutions in over 70 countries. Our mission is to unlock the next era of financial, creative, and personal freedom by providing trusted access to the decentralized future. We envision a world where crypto reshapes the global financial system, internet, and money to create greater choice, independence, and opportunity for all â bridging traditional finance with the emerging cryptoeconomy in a way that is more open, fair, and secure. As a publicly traded company, Gemini is poised to accelerate this vision with greater scale, reach, and impact.
The Department: Data
What you'll work on
- 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 Geminiâs defenses.
What we're looking for
- ââBachelorâs 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.