Pytorch Jobs in New York, New York, United States (1 New)

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Senior Data Scientist Machine Learning Risk

Gemini New York, New York; San Francisco, California

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$140k - $200k · Senior Full-time Posted 1 day ago 5 min read

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

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 â 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 weâre united by a shared mission: to unlock the full potential of Geminiâs data to drive growth, efficiency, and customer impact.

The Role: Senior Data Scientist, Machine Learning (Risk)

As a Senior Data Scientist focused on Machine Learning for Risk, youâll play a key role in protecting our customers and platform. Youâll work cross-functionally with product, engineering, and operations to design and deploy models that detect, prevent, and mitigate fraud risk across Geminiâs ecosystem. Youâll own the full machine learning lifecycle from identifying fraud signals and engineering features to training, evaluating, and deploying models in production. Youâll 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.

This role is required to be in person twice a week at either our San Francisco 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.
Stay current on emerging fraud tactics and machine learning approaches to continually evolve Geminiâs defenses.

Minimum Qualifications:

Bachelorâs 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 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).
Excellent communication skills and the ability to translate complex technical concepts into actionable insights.

Preferred Qualifications:

Masterâs 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.

It Pays to Work Here

The compensation & benefits package for this role includes:

Competitive starting salary
A discretionary annual bonus
Long-term incentive in the form of a new hire equity grant
Comprehensive health plans
401K 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 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 candidateâs 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.

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