Principal Data Scientist, Machine Learning (Growth): Gemini

Gemini, a global crypto and Web3 platform, is hiring a Principal Data Scientist for its Machine Learning (Growth) team. This is a high-impact, hands-on individual contributor role focused on improving the customer experience, from onboarding to new product adoption.

You will own the full machine learning lifecycle, from identifying growth signals and engineering features to training and deploying models in production. You will partner with stakeholders across Marketing, Exchange Growth, and Credit Card to profitably improve customer growth, with opportunities for technical leadership and mentorship.

Responsibilities
Analyze large, complex datasets to identify opportunities to improve onboarding and product adoption.

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.

Evaluate model performance through experiments, backtesting, and continuous monitoring.

Partner with product managers, engineers, and customer service operations to translate model outputs into effective growth strategies.

Mentor and guide more junior data scientists and machine learning engineers, leading code and design reviews.

Help recruit and onboard new talent.

Requirements
Minimum Qualifications:

10+ years of experience (7+ years with a 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.

Strong proficiency in Python, relevant modeling libraries (e.g., scikit-learn, xgboost, TensorFlow, PyTorch), and SQL.

Experience with data processing and model lifecycle tools such as Databricks, SageMaker, Snowflake, or MLflow.

Familiarity with orchestration and data pipeline frameworks (e.g., Airflow, Spark).

Preferred Qualifications:

Master’s degree in a quantitative field.

Domain expertise in crypto / blockchain / Web3 data (on-chain data, DeFi protocols, etc.).

Experience with lifetime value, marketing mix, or product recommendation models in fintech, banking, or crypto.

Understanding of model governance, interpretability, and fairness in regulated financial contexts.

Proven experience in recruiting, mentoring, and leading design discussions.