O que a vaga pede
About Cashera Cashera is a fast-growing fintech and lending company that provides funding solutions to businesses across multiple industries in the United States and UK. Cashera combines technology, data-driven decision-making, and industry expertise to deliver fast, flexible financing options that traditional lenders often cannot provide. The Role As a Data Scientist on the Credit & Fraud Risk Team, you will be building predictive machine learning models for credit underwriting and fraud detection. You'll get an opportunity to build models and systems that evaluate borrower creditworthiness, mitigate operational loss, and intercept real-time fraud vectors. This is an excellent opportunity for someone early in their Risk and Data Science career, and you will be owning end-to-end deliverables while collaborating with Engineering, Product, Collections, and Finance teams. Key Responsibilities Model Development & Lifecycle: Build, evaluate, and scale predictive models using XGBoost, LightGBM, and Deep Learning for underwriting, credit line assignment, and collections. Familiarity with GenAI techniques for feature creation and transaction tagging on bank transactional data is a plus. Credit Risk Modeling: Build credit risk models for sub-prime and near-prime customers in a fintech environment with short model build cycles. Fraud & Risk Defense: Design and deploy real-time decisioning rules and ML systems to detect synthetic fraud, digital identity theft, account takeovers, and transactional fraud. Feature Engineering: Mine complex, large-scale, and alternative data streams (bank transactional data, logs, credit bureau reports, structured and unstructured digital signals) to extract predictive signals. Portfolio Optimization: Partner with Credit Risk Strategists to translate model outputs into actionable credit limits, cutoff thresholds, and loss-forecasting simulations. Platform Architecture: Collaborate with Data Platform Engineers to build reliable pipelines and high-throughput feature extraction. Communication: Raise the technical bar through reusable tooling and proactive communication to risk leadership and partners. What You'll Bring Education: Degree in a quantitative field (Statistics, Mathematics, Computer Science, Economics, Data Science) or equivalent practical experience. Experience: 2-4 years of Data Science experience, ideally in Credit Risk, Fraud Analytics, Lending, or Fintech. Technical Toolbox: Fluency in Python and advanced, highly analytical