Senior Machine Learning Data Scientist in United States at Jobgether

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior Machine Learning Data Scientist in United States.

This role sits at the core of a high-impact fraud and risk intelligence function, where machine learning is used to protect millions of users and transactions in a fast-scaling digital ecosystem. You will design, build, and deploy advanced ML models that detect fraud patterns, assess risk, and optimize post-purchase protection systems. The position spans the full data science lifecycle, from problem framing and feature engineering to production deployment and monitoring. You will work closely with product, engineering, and fraud operations teams to translate complex behavioral signals into scalable machine learning solutions. This is a highly collaborative and impact-driven environment where your models directly influence business performance, customer trust, and fraud prevention effectiveness. You will also help shape best practices in experimentation, model evaluation, and production ML systems.

• Own the end-to-end machine learning model lifecycle, including problem definition, feature engineering, experimentation, training, evaluation, and production monitoring
• Design and develop fraud detection and risk assessment models using large-scale transactional and behavioral datasets
• Translate complex fraud and user behavior patterns into well-defined ML problems and measurable success criteria
• Build and maintain scalable feature engineering pipelines to support production-grade machine learning systems
• Collaborate with ML engineers on model deployment, infrastructure integration, and production readiness
• Monitor model performance in production, identifying data drift, degradation, and retraining needs
• Partner with product, engineering, fraud operations, and leadership teams to define fraud prevention strategies
• Conduct rigorous experimentation to improve model accuracy, precision, and recall in real-world environments
• Document models, methodologies, and evaluation results to ensure transparency and reproducibility
• Contribute to a culture of continuous improvement, experimentation, and data-driven decision-making

Requirements:

• 3+ years of experience building and deploying machine learning models into production environments
• Strong proficiency in Python and SQL for data manipulation, modeling, and analysis
• Solid understanding of machine learning fundamentals including model selection, evaluation methods, and feature engineering
• Hands-on experience with ML frameworks such as PyTorch, scikit-learn, and XGBoost or similar tools
• Experience working with large-scale datasets and real-world production ML systems
• Strong analytical thinking with deep curiosity about user behavior and fraud patterns
• Ability to translate ambiguous business problems into structured ML solutions
• Experience with model monitoring, performance tracking, and lifecycle management is a plus
• Exposure to fraud detection, risk modeling, or security-related ML systems is highly desirable
• Familiarity with cloud ML platforms such as AWS (e.g., SageMaker) is a plus
• Experience with graph-based modeling or observability tools is an advantage
• Bachelor’s degree or higher in a quantitative field such as Computer Science, Mathematics, Engineering, Physics, or related discipline
• Strong collaboration skills, with an empathetic and team-oriented mindset

Benefits:

• Competitive annual salary ranging from 135,000 to 165,000 USD depending on experience
• Equity participation in a fast-growing startup environment
• Comprehensive medical, dental, and vision insurance coverage
• Flexible paid time off policy supporting work-life balance
• 401(k) retirement plan with financial guidance support
• Opportunity to work on high-impact machine learning systems at scale
• Collaborative, innovative, and fast-paced team culture
• Remote work flexibility within the continental United States