Gemini
Senior Data Scientist, Machine Learning (Risk)
Gemini, San Francisco, California, United States, 94199
Senior Data Scientist, Machine Learning (Risk)
Join to apply for the
Senior Data Scientist, Machine Learning (Risk)
role at
Gemini .
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: Senior Data Scientist, Machine Learning (Risk) This role is required to be in person twice a week at either our San Francisco or New York City, NY office.
As a Senior 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 will own the full machine learning lifecycle – from identifying fraud signals and engineering features to training, evaluating and deploying models in production. You will 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.
Responsibilities
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 behavioural profiling.
Evaluate model performance through experiments, back‑testing and continuous monitoring to improve capture rates and reduce false positives.
Stay current on emerging fraud tactics and machine learning approaches to continually evolve Gemini’s defenses.
Minimum Qualifications
Bachelor’s degree in Computer Science, Data Science, Statistics or a related field.
5+ years of experience (3+ years with PhD) applying data science and machine learning to financial, payments or fraud‑related problems.
1+ 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 modelling 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).
Excellent communication skills and the ability to translate complex technical concepts into actionable insights.
Preferred Qualifications
Master’s degree or equivalent experience in a quantitative field.
Experience with fraud modelling, risk scoring or anomaly detection in fintech, banking or crypto.
Familiarity with blockchain data and on‑chain analytics for detecting illicit activity.
Understanding of model governance, interpretability and fairness in regulated financial contexts.
Compensation & Benefits
Competitive starting salary
A discretionary annual bonus
Long‑term incentive in the form of a new hire equity grant
Comprehensive health plans
401(k) with company matching
Paid parental leave
Flexible time off
Salary Range: The base salary range for this role is between $140,000 – $200,000 in New York, California and Washington. This range does not include discretionary bonus or equity. Compensation is determined based on skillset, experience, job scope and current market data.
Work Approach 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; 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.
Equal Employment Opportunity Statement 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.
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Senior Data Scientist, Machine Learning (Risk)
role at
Gemini .
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: Senior Data Scientist, Machine Learning (Risk) This role is required to be in person twice a week at either our San Francisco or New York City, NY office.
As a Senior 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 will own the full machine learning lifecycle – from identifying fraud signals and engineering features to training, evaluating and deploying models in production. You will 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.
Responsibilities
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 behavioural profiling.
Evaluate model performance through experiments, back‑testing and continuous monitoring to improve capture rates and reduce false positives.
Stay current on emerging fraud tactics and machine learning approaches to continually evolve Gemini’s defenses.
Minimum Qualifications
Bachelor’s degree in Computer Science, Data Science, Statistics or a related field.
5+ years of experience (3+ years with PhD) applying data science and machine learning to financial, payments or fraud‑related problems.
1+ 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 modelling 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).
Excellent communication skills and the ability to translate complex technical concepts into actionable insights.
Preferred Qualifications
Master’s degree or equivalent experience in a quantitative field.
Experience with fraud modelling, risk scoring or anomaly detection in fintech, banking or crypto.
Familiarity with blockchain data and on‑chain analytics for detecting illicit activity.
Understanding of model governance, interpretability and fairness in regulated financial contexts.
Compensation & Benefits
Competitive starting salary
A discretionary annual bonus
Long‑term incentive in the form of a new hire equity grant
Comprehensive health plans
401(k) with company matching
Paid parental leave
Flexible time off
Salary Range: The base salary range for this role is between $140,000 – $200,000 in New York, California and Washington. This range does not include discretionary bonus or equity. Compensation is determined based on skillset, experience, job scope and current market data.
Work Approach 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; 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.
Equal Employment Opportunity Statement 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.
#J-18808-Ljbffr