Associate Data Scientist, Lending Club

• 2+ years of experience in data science, fraud analytics, decision science, or a related analytical role
• Bachelor's degree or higher, or equivalent combination of education and experience
• Hands-on experience using SQL and a statistical programming language such as Python to work with large datasets and produce reproducible analysis
• Working knowledge of statistical analysis, data visualization, and model or strategy performance measurement
• Ability to translate analytical findings into clear recommendations for technical and business audiences
• You take ownership of assigned work, ask thoughtful questions, and incorporate feedback to improve quality and outcomes
• You use sound judgment when handling sensitive data and applying new methods, balancing speed with accuracy, explainability, and risk
• You have practical familiarity with AI tools, understand their strengths and limitations, and are ready to apply that judgment to real-world analytical problems
• You collaborate with humility, adapt to changing priorities, and bring curiosity, integrity, and a problem-solving mindset

Desirable

• The Fraud and Data Science team uses internal and external data to detect, prevent, and mitigate fraud across Happen Bank. As an Associate Data Scientist, you’ll turn complex data into actionable insights, develop analytical solutions that strengthen fraud strategies, and partner across Fraud, Risk, Product, Technology, and Operations while building deeper technical and business expertise
• Analyze large, complex datasets using SQL and Python to identify fraud patterns, emerging risks, and opportunities to improve decision-making
• Support the development and implementation of fraud detection strategies, rules, and models based on data analysis and business requirements
• Monitor fraud trends, loss metrics, strategy performance, and key indicators, investigating anomalies and translating findings into actionable insights
• Build and maintain reliable queries, dashboards, and reporting using established data and business intelligence standards
• Evaluate new internal and external data sources, features, and analytical techniques for predictive value, data quality, and practical application
• Develop repeatable processes to monitor model and strategy performance, data accuracy, and changes in population behavior
• Partner with cross-functional teams to translate business questions into analytical approaches and support solutions from analysis through implementation
• Use AI-assisted tools where appropriate to accelerate exploration, coding, and documentation while validating outputs and protecting sensitive data