Remote Staff Data Scientist

Role Overview

The Senior Data Scientist specializes in risk analytics, fraud detection, and Generative AI within a platform-focused environment.

They own the end-to-end development of data-driven solutions, including data exploration, modeling, GenAI agent design, deployment, and monitoring.

Their work involves creating scalable data pipelines, developing advanced analytics, and building innovative GenAI tools to automate workflows, enhance fraud prevention, and improve decision accuracy.

The role requires collaboration with engineering, product, and risk teams to deliver actionable insights and ensure solutions adhere to privacy and security standards.

They mentor other data scientists, communicate complex concepts clearly, and contribute to establishing best practices in AI explainability and risk evaluation.

Key Responsibilities

• Develop analytics to understand user behavior, fraud patterns, and workflow effectiveness.
• Build scalable data pipelines and ML workflows for production use.
• Design and evaluate GenAI solutions, including LLM-based applications and agents.
• Define evaluation frameworks and ensure operational robustness of models and AI tools.
• Collaborate on data science infrastructure and deploy solutions with monitoring and feedback loops.
• Create tools for self-serve analytics and facilitate decision-making for non-technical users.
• Partner with cross-functional teams to translate business needs into data solutions.
• Mentor team members and promote best practices in data science and AI safety.
• Ensure compliance with data privacy, security, and regulatory standards.
• Communicate findings and technical concepts effectively to diverse stakeholders.

Qualifications & Skills

• Master’s or PhD in a quantitative field or equivalent experience.
• 6+ years in data science, machine learning, or data engineering, with expertise in fraud/risk analytics.
• Proven experience applying Generative AI in production, including building and evaluating LLM applications.
• Strong proficiency in Python, SQL, and ML frameworks (e.g., TensorFlow, PyTorch).
• Experience with data pipelines, deployment, and monitoring in high-volume, real-time environments.
• Knowledge of privacy-preserving techniques and regulated environments.
• Excellent collaboration, communication, and product-minded skills.