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 opportunities to proactively improve onboarding and product adoption opportunities and engineer predictive features using internal and external data sources.
- Design, train, and deploy machine learning models to identify growth opportunities, including lifetime value, marketing channel optimization, and product cross-sell models.
- Build and maintain end-to-end data and model pipelines for marketing and growth, including onboarding & adoption anomaly detection and behavioral profiling of growth drivers.
- Evaluate model performance through experiments, backtesting, and continuous monitoring to improve adoption rates and improve customer acquisition cost.
- Partner with product managers, engineers, and customer service operations to translate model outputs into effective growth strategies and user-facing features.
- Communicate findings and recommendations to technical and non-technical audiences, influencing strategy and prioritization.
- Mentor and guide more junior and mid-level data scientists & machine learning engineers: lead code reviews, design reviews, and best practice evangelism.
- Help recruit and onboard new talent, shaping the future of Geminiâs machine learning discipline.
- Stay up to date on new tools, technologies, and machine learning approaches, bringing proposals and proof-of-concepts when appropriate.
What we're looking for
- 10+ years of experience (7+ years with PhD) applying data science and machine learning in financial, payments, or B2C platforms.
- 5+ 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.