Senior Applied Scientist, Credit Risk
Core
Design, build, and optimize machine learning models for credit risk decisioning and portfolio management to enable faster, smarter, and more scalable risk decisions for businesses.
Role type
Senior Applied Scientist (Credit Risk)
Builds
Production credit risk models and validation frameworks for a B2B financial infrastructure platform serving 50,000+ companies.
Domain
Fintech / Credit Risk / Machine Learning
Deliverable
production ML models
Required skills
Advanced statistics, machine learning, optimization, economics, Python, SQL, large dataset handling, predictive modeling, exploratory data analysis, model deployment and monitoring, backtesting and validation
Preferred skills
PhD in quantitative fields, data science engineering development cycle expertise, data orchestration platforms (Airflow, Dagster, Prefect), high-growth startup experience, AI/LLM application
Technologies
Python, NumPy, pandas, scikit-learn, PyTorch, SQL, Airflow, Dagster, Prefect
Responsibilities
Design and optimize ML models for credit risk; own the full applied science lifecycle from data exploration to production monitoring; investigate and integrate new data sources; develop backtesting and validation frameworks; apply ML, statistics, causal inference, and optimization to business problems; communicate data-driven insights to influence strategy; partner with stakeholders to define objectives and roadmaps; contribute to best practices for model development and production reliability
Seniority
Senior, hands-on IC