Data Scientist (Risk)

Join Imprint, a fast-growing fintech company, as a Data Scientist in the Risk team. You will be responsible for improving credit decision-making processes, building models, and analyzing data to optimize approval rates while maintaining credit quality. You will collaborate with various teams, including Credit Strategy, Product, Engineering, and Marketing, to develop targeting models and evaluate channel-level credit performance. The ideal candidate will have 5 to 8+ years of experience in data science or risk analytics, strong Python and SQL skills, and a deep understanding of statistical inference and experimentation design.

• Posséder et améliorer l'ensemble du pipeline de décision de crédit en amont, y compris le scoring des demandes, les règles de politique, les cascades de refus et l'optimisation des taux d'approbation.
• Construire et itérer sur des modèles de souscription, de ciblage et de segmentation qui élargissent les approbations sûres et améliorent la qualité d'acquisition au niveau des canaux.
• Concevoir et analyser des tests A/B et des expériences champion/challenger sur les politiques de crédit, en établissant un rythme de test et d'apprentissage avec des lectures structurées sur les performances d'acquisition et de crédit.
• Comfort owning projects end-to-end in a fast-moving startup environment with limited scaffolding, collaborating cross-functionally with Policy, Strategy, Product, and Engineering
• We don't expect every candidate to check every box. If this role excites you and you bring strong fundamentals, we encourage you to apply
• Experience building credit risk or targeting models (scorecards, underwriting models, segmentation) or similar predictive modeling in a regulated environment
• 5 to 8+ years of experience in data science, risk analytics, or a related quantitative field, ideally at a high-growth startup or fintech company
• Full-stack problem-solving orientation: you dive into messy data, trace a decline to its root cause, and question assumptions in pursuit of a better answer
• Ability to present complex findings clearly to technical and non-technical audiences, including senior leadership and external partner stakeholders
• Deep understanding of statistical inference, experimentation design, and causal analysis, with the ability to disentangle policy impact from population shifts and channel mix changes
• Strong Python and SQL skills, with the ability to build models, transform raw data, and create custom datasets from complex financial data
• Comfort with AI tools and AI-native workflows; you actively use tools like Claude, Copilot, or similar to accelerate your work and are excited to build AI-powered analytical systems
• Experience with credit card underwriting, lending, or consumer credit products
• Familiarity with credit bureau data (Vantage, FICO, tradeline attributes) and alternative data sources
• Experience building or scaling experimentation infrastructure for credit policy testing
• Exposure to fraud detection, KYC/IDV workflows, or application fraud models
• Understanding of acquisition channel economics and experience partnering with marketing or credit strategy teams on targeting and LTV modeling