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Senior Fraud Data Scientist

Posted about 1 month agoViewed

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💎 Seniority level: Senior, 3+ years

📍 Location: United States

💸 Salary: 135000.0 - 165000.0 USD per year

🔍 Industry: E-commerce

🏢 Company: Extend👥 51-100💰 $40,000,000 Series B almost 4 years agoMobile PaymentsCredit CardsPaymentsFinTechSoftware

⏳ Experience: 3+ years

🪄 Skills: PythonSQLData AnalysisMachine LearningNumpyData sciencePandas

Requirements:
  • 3+ years of experience in fraud analytics, ideally within an e-commerce or retail environment
  • Prior experience in a startup or high-growth environment is preferred
  • Bachelor’s degree or higher in a quantitative field such as Mathematics, Statistics, Computer Science, Engineering, Operations Research, Physics or related field
  • Strong grasp of core data science and analytics concepts, including statistical analysis, modeling, and data wrangling
  • Proficiency in SQL and experience in Python (pandas, NumPy, scikit-learn, etc.)
  • Exceptional communication and stakeholder management skills, with a proven ability to work cross-functionally and influence outcomes
  • High attention to detail, strong intellectual curiosity, and a deep understanding of user behavior and fraud patterns
  • Empathetic, humble, and collaborative team player
Responsibilities:
  • Analyze large-scale behavioral, transactional, and interaction data to uncover signals indicative of fraud and abuse
  • Design and implement rules engines, heuristics, and machine learning models to automatically detect and prevent fraudulent activity
  • Apply strong business acumen to rapidly identify actionable insights, analyze fraud patterns, and validate hypotheses
  • Influence business cases and pricing strategies, and contribute to proof-of-concept initiatives with prospective merchants
  • Respond swiftly to emerging fraud threats by developing monitoring frameworks, dashboards, and mitigation solutions in collaboration with cross-functional teams
  • Partner closely with leadership, go-to-market, fraud operations, product, and engineering teams to define and execute effective fraud strategies
  • Champion a culture of continuous learning, experimentation, and collaboration across the fraud and broader data science teams
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