Data Scientist (Credit Risk Analytics)

Join Prosper, a leading fintech company, as a Data Scientist in the Credit Risk Analytics vertical. In this hybrid role, you will build and deploy predictive models, shape credit risk strategy, and analyze portfolio performance. You will collaborate closely with engineering, develop internal tools, and monitor credit risk models in production. Enjoy a range of benefits, including stock options, a 401K retirement savings plan, comprehensive health insurance, and a monthly fitness stipend.

• Contribuer de manière significative à l'équipe de risque de crédit en développant et en déployant des modèles prédictifs de pointe.
• Collaborer étroitement avec les équipes d'ingénierie pour déployer les modèles dans un environnement de production.
• Analyser les performances du portefeuille et identifier les tendances pour améliorer les décisions de risque.

• Expert knowledge of statistical programming languages (e.g., Python) and database languages (e.g., SQL)
• Strong ability to collaborate seamlessly with people across various functions (engineering, product, compliance) and build strong relationships
• 2-3+ years of work experience in fintech, finance, or another high-impact field applying statistical and machine learning predictive techniques. Consumer lending experience in unsecured personal loans or credit cards is a strong plus
• Strong communication skills with the ability to translate complex technical subject matter into clear, actionable business strategies for cross-functional partners and senior management
• Advanced degree (M.S./Ph.D.) preferably in statistics, computer science, engineering, physical sciences, economics, or a related technical field
• Self-motivated, results-oriented, enthusiastic, and a creative thinker who bridges the gap between data science and business strategy
• Ability to innovate within regulatory guidelines with a strong commitment to reproducible research and model governance
• Solid understanding of coding best practices, model documentation, and ML ops principles
• Ability to work unsupervised in a fast-paced environment, effectively prioritizing among parallel technical and strategic projects