Note: The job is a remote job and is open to candidates in USA. Cashera is a fintech and lending company that provides funding solutions to businesses in the United States and UK. The company is seeking a Data Scientist to build predictive machine learning models and real-time systems for credit underwriting, fraud detection, loss mitigation, and portfolio optimization while collaborating with cross-functional teams.
Responsibilities
- 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
- Build credit risk models for sub-prime and near-prime customers in a fintech environment with short model build cycles
- Design and deploy real-time decisioning rules and ML systems to detect synthetic fraud, digital identity theft, account takeovers, and transactional fraud
- Mine complex, large-scale, and alternative data streams (bank transactional data, logs, credit bureau reports, structured and unstructured digital signals) to extract predictive signals
- Partner with Credit Risk Strategists to translate model outputs into actionable credit limits, cutoff thresholds, and loss-forecasting simulations
- Collaborate with Data Platform Engineers to build reliable pipelines and high-throughput feature extraction
- Raise the technical bar through reusable tooling and proactive communication to risk leadership and partners
Skills
- Degree in a quantitative field (Statistics, Mathematics, Computer Science, Economics, Data Science) or equivalent practical experience
- 2-4 years of Data Science experience, ideally in Credit Risk, Fraud Analytics, Lending, or Fintech
- Fluency in Python and advanced, highly analytical SQL
- Ability to clearly present complex results (e.g., ROC-AUC curves, model drift scenarios) to non-technical stakeholders
- Familiarity with GenAI techniques for feature creation and transaction tagging on bank transactional data is a plus
Company Overview