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 — 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 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. The Role: Principal Data Scientist, Machine Learning (Growth) As a Principal Data Scientist focused on Machine Learning for Growth, you’ll play a key role in improving our customer experience from onboarding to new product adoption. You’ll work cross-functionally with product, engineering, and operations to design and deploy models that improve customer onboarding and product adoption across Gemini’s ecosystem. You’ll own the full machine learning lifecycle from identifying growth signals and engineering features to training, evaluating, and deploying models in production. You’ll partner with stakeholders across Marketing, Exchange Growth, and Credit Card to profitably improve customer growth. 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 our San Francisco or New York City. Responsibilities: <ul> <li>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.</li> <li>Design, train, and deploy machine learning models to identify growth opportunities, including lifetime value, marketing channel optimization, and product cross-sell models.</li> <li>Build and maintain end-to-end data and model pipelines for marketing and growth, including onboarding &amp; adoption anomaly detection and behavioral profiling of growth drivers.</li> <li>Evaluate model performance through experiments, backtesting, and continuous monitoring to improve adoption rates and improve customer acquisition cost.</li> <li>Partner with product managers, engineers, and customer service operations to translate model outputs into effective growth strategies and user-facing features.</li> <li>Communicate findings and recommendations to technical and non-technical audiences, influencing strategy and prioritization.</li> <li>Mentor and guide more junior and mid-level data scientists &amp; machine learning engineers: lead code reviews, design reviews, and best practice evangelism.</li> <li>Help recruit and onboard new talent, shaping the future of Gemini’s machine learning discipline.</li> <li>Stay up to date on new tools, technologies, and machine learning approaches, bringing proposals and proof-of-concepts when appropriate.</li> </ul> Minimum Qualifications: <ul> <li>10+ years of experience (7+ years with PhD) applying data science and machine learning in financial, payments, or B2C platforms.</li> <li>5+ years of experience developing, deploying, and maintaining production-grade ML models, ideally for real-time or large-scale applications.</li> <li>Strong proficiency in Python and relevant modeling libraries (eg, scikit-learn, xgboost, TensorFlow, PyTorch) and SQL.</li> <li>Experience with data processing and model lifecycle tools such as Databricks, SageMaker, Snowflake, MLflow, or similar.</li> <li>Familiarity with orchestration and data pipeline frameworks (e.g., Airflow, Spark).</li> <li>Demonstrated ability to work cross-functionally with product, engineering, and operations teams.</li> <li>Excellent communication skills and the ability to translate complex technical concepts into actionable insights.</li> </ul> Preferred Qualifications: <ul> <li>Master’s degree or equivalent experience in a quantitative field.</li> <li>Domain expertise in crypto / blockchain / Web3 data (on-chain data, DeFi protocols, transaction analytics).</li> <li>Experience with lifetime value, marketing mix, or product recommendation models in fintech, banking, or crypto.</li> <li>Understanding of model governance, interpretability, and fairness in regulated financial contexts.</li> <li>Experience mentoring data scientists / machine learning engineers or contributing to technical best practices within a team.</li> <li>Proven experience in recruiting, mentoring, leading design discussions, and influencing data science and machine learning best practices across teams.</li> </ul> <div>It Pays to Work Here</div> <div> </div> <div>The compensation &amp; benefits package for this role includes:</div> <ul> <li>Competitive starting salary</li> <li>A discretionary annual bonus</li> <li>Long-term incentive in the form of a new hire equity grant</li> <li>Comprehensive health plans</li> <li>401K with company matching</li> <li>Paid Parental Leave</li> <li>Flexible time off</li> </ul> Salary Range: The base salary range for this role is between $192,500 - $275,000 in the State of New York, the State of California and the State of Washington. This range is not inclusive of our discretionary bonus or equity package. When determining a candidate’s compensation, we consider a number of factors including skillset, experience, job scope, and current market data. In the United States, we offer a hybrid work approach at our hub offices, balancing the benefits of in-person collaboration with the flexibility of remote work. Expectations may vary by location and role, so candidates are encouraged to connect with their recruiter to learn more about the specific policy for the role. Employees who do not live near one of our hubs are part of our remote workforce. At Gemini, we strive to build diverse teams that reflect the people we want to empower through our products, and we are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Equal Opportunity is the Law, and Gemini is proud to be an equal opportunity workplace. If you have a specific need that requires accommodation, please let a member of the People Team know. #LI-PA1