Nifty Gateway Studio
Staff Data Scientist, Machine Learning (Risk)
Nifty Gateway Studio, San Francisco, California, United States, 94199
Staff Data Scientist, Machine Learning (Risk)
Join to apply for
Staff Data Scientist, Machine Learning (Risk)
role at
Gemini , a global crypto and Web3 platform.
Location:
New York, NY and San Francisco, CA – hybrid/remote options available.
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.
The Role As a Staff Data Scientist focused on Machine Learning for Risk, you will play a key role in protecting customers and the 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. This high‑impact, hands‑on individual contributor role offers opportunities for technical leadership and mentorship.
Required in‑person presence twice a week at either our San Francisco, CA or New York City, NY office.
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 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 Gemini’s defenses.
Minimum Qualifications
Bachelor’s 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 (e.g., 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.
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.
Experience mentoring data scientists / machine learning engineers or contributing to technical best practices within a team.
Benefits
Competitive starting salary.
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 $168,000 – $240,000 in New York, California, and Washington states, not including discretionary bonus or equity package.
Equal Opportunity Statement In the United States, we offer a hybrid work approach at our hub offices, balancing in‑person collaboration with remote flexibility. Employees who do not live near one of our hubs are part of our remote workforce. Gemini is 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. If you have a specific need that requires accommodation, please let a member of the People Team know.
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Staff Data Scientist, Machine Learning (Risk)
role at
Gemini , a global crypto and Web3 platform.
Location:
New York, NY and San Francisco, CA – hybrid/remote options available.
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.
The Role As a Staff Data Scientist focused on Machine Learning for Risk, you will play a key role in protecting customers and the 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. This high‑impact, hands‑on individual contributor role offers opportunities for technical leadership and mentorship.
Required in‑person presence twice a week at either our San Francisco, CA or New York City, NY office.
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 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 Gemini’s defenses.
Minimum Qualifications
Bachelor’s 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 (e.g., 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.
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.
Experience mentoring data scientists / machine learning engineers or contributing to technical best practices within a team.
Benefits
Competitive starting salary.
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 $168,000 – $240,000 in New York, California, and Washington states, not including discretionary bonus or equity package.
Equal Opportunity Statement In the United States, we offer a hybrid work approach at our hub offices, balancing in‑person collaboration with remote flexibility. Employees who do not live near one of our hubs are part of our remote workforce. Gemini is 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. If you have a specific need that requires accommodation, please let a member of the People Team know.
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