Data Scientist- Risk & Fraud

About the Role
This Staff Data Scientist role sits at the intersection of platform data science, fraud and risk analytics, and Generative AI. The position is responsible for end-to-end development of data-driven solutions across a risk management platform, spanning data exploration and preparation, machine learning, AI agent development, production deployment, and ongoing monitoring.

The role combines expertise in fraud detection and risk decisioning with experience designing and operationalizing LLM-powered applications that improve analytics workflows, investigations, automation, and decision support.

Working closely with engineering, product, risk, and business stakeholders, this individual will help build scalable AI and machine learning solutions that deliver actionable insights, self-service analytics, and advanced fraud prevention capabilities. This is a highly collaborative, hands-on leadership role for someone who enjoys owning complex data problems from concept through production.

Key Responsibilities

Data Science & Analytics

• Analyze large, complex, and noisy datasets to uncover insights related to user behavior, platform usage, fraud trends, risk performance, and operational workflows.
• Translate analytical findings into recommendations that improve product strategy, risk management, and business outcomes.
• Design and execute experimentation frameworks to measure feature effectiveness, workflow optimization, and user engagement.

Machine Learning & AI Development

• Build and deploy advanced machine learning and risk models to support fraud detection, decisioning, and operational intelligence.
• Design and implement Generative AI applications, including LLM-powered assistants, workflow automation tools, and analytics copilots.
• Develop evaluation frameworks for AI and machine learning systems, including benchmark testing, safety reviews, hallucination detection, and performance measurement.
• Create tools for model validation, backtesting, stress testing, and resilience assessment in high-risk environments.

Platform & Infrastructure

• Architect scalable data pipelines and production ML workflows in collaboration with engineering teams.
• Develop and maintain core AI infrastructure such as feature stores, model-serving APIs, evaluation services, monitoring systems, and feedback loops.
• Own production deployment of ML and AI applications, including observability, alerting, reliability standards, and service-level objectives.
• Support both batch and real-time data processing environments.

Product & Cross-Functional Leadership

• Partner with product, engineering, risk, consulting, and customer-facing teams to identify opportunities and deliver data-driven solutions.
• Enable non-technical users through dashboards, self-service analytics, reusable frameworks, and AI-powered assistants.
• Mentor data scientists and analysts while promoting best practices in experimentation, machine learning engineering, AI governance, and model evaluation.
• Communicate technical findings and strategic recommendations clearly to both technical and executive audiences.

Governance & Compliance

• Ensure AI and analytics solutions adhere to privacy, security, and regulatory requirements when handling sensitive data.
• Contribute to organizational standards for explainability, monitoring, model governance, feature logging, and documentation.

Qualifications

• Master's degree or PhD in Computer Science, Statistics, Machine Learning, Engineering, or a related quantitative field, or equivalent industry experience.
• 6+ years of experience in data science, machine learning, analytics, or large-scale data engineering environments.
• Proven experience developing fraud prevention, risk analytics, trust and safety, or decision intelligence solutions.
• Strong experience building and deploying Generative AI and LLM-powered applications in production environments.
• Experience developing AI assistants, retrieval-augmented generation (RAG) systems, workflow automation tools, or knowledge-based applications.
• Expertise in prompt engineering, model evaluation, AI safety, and LLM performance optimization.
• Advanced proficiency in Python and SQL.
• Hands-on experience with machine learning frameworks such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Experience with modern AI tooling, model APIs, orchestration frameworks, and open-source AI ecosystems.
• Strong background building scalable data pipelines and production ML systems.
• Experience with modern data platforms, distributed processing frameworks, and large-scale analytics environments.
• Knowledge of ETL processes, data warehousing, distributed computing, and data modeling principles.
• Familiarity with feature stores, model serving infrastructure, ML CI/CD, monitoring, and feedback systems.
• Strong communication skills and ability to collaborate across technical and business teams.
• Product-oriented mindset with a focus on measurable business impact and user outcomes.

Preferred Qualifications

• Experience with fraud detection, risk modeling, trust and safety, or identity-related use cases.
• Experience working with workflow orchestration, case management, or decision automation platforms.
• Experience mentoring senior individual contributors and helping define technical direction for data science organizations.