AI Fraud and Risk Automation Analyst (remote)

CrowdStrike CrowdStrike · Enterprise · United States · Remote

This role focuses on building and maintaining AI-powered systems for fraud and risk detection within an eCommerce context. It involves data engineering (Snowflake, SQL, Python), developing rules-based engines, and integrating machine learning models and LLMs to identify fraudulent activity and protect intellectual property. The role requires analyzing large datasets, automating workflows, and collaborating with various teams to improve risk posture.

What you'd actually do

  1. Parse and analyze large volumes of transactional and behavioral data to identify indicators of fraud and risk
  2. Develop and maintain a rules-based risk engine that adapts to emerging threat patterns
  3. Design, build, and maintain Snowflake tables, views, and data pipelines to support risk detection workflows
  4. Leverage AI tools (including LLMs) to enhance fraud detection capabilities and generate risk insights
  5. Monitor the risk engine and Slack channels for real-time alerts and triage incidents as they unfold

Skills

Required

  • 6+ months of experience managing a risk engine or fraud detection system in an eCommerce or SaaS environment
  • Strong SQL skills with experience querying and managing data in Snowflake or similar cloud data warehouses
  • Proficiency in Python for data analysis, automation, and scripting
  • Cybersecurity knowledge: Strong understanding of adversarial tactics, fraud vectors, and threat intelligence
  • Analytical mindset: Ability to think like an adversary, identify patterns in complex datasets, and make data-driven decisions under pressure
  • Communication skills: Excellent written and verbal communication skills with the ability to translate technical findings into business recommendations
  • Self-starter mentality: Proven ability to work independently, prioritize tasks, and meet deadlines in a fast-paced environment

Nice to have

  • Experience with AI/LLM tools (e.g., ChatGPT, Claude, or similar) for data analysis, threat intelligence, or automation
  • Familiarity with machine learning concepts and anomaly detection techniques
  • Experience building and maintaining Snowflake data models, including tables, views, and stored procedures
  • Knowledge of eCommerce fraud vectors such as account takeover, payment fraud, trial abuse, and bot attacks
  • Understanding of the SaaS business model and cloud-based platform sales
  • Experience with data visualization tools (e.g., Tableau, Looker, or similar)
  • Familiarity with IPQS, Stripe, or other fraud detection APIs
  • Background in threat intelligence, incident response, or SOC operations

What the JD emphasized

  • demonstrable track record of using rules-based approaches to prevent unauthorized access

Other signals

  • AI-assisted detection models
  • automated workflows
  • AI tools (including LLMs) to enhance fraud detection
  • implement machine learning models for anomaly detection
  • AI-assisted analysis to identify patterns in unstructured data