Senior Applied Scientist Credit Risk

Skills

About the Role

You will design, build, and optimize machine-learning models that support credit-risk decisioning and portfolio management. You will own the applied-science lifecycle, including data exploration, feature development, prototyping, deployment, monitoring, and iteration. You will evaluate new structured and unstructured data sources, develop validation and backtesting frameworks, and apply machine learning, statistics, causal inference, optimization, and economics to business problems. You will communicate data-driven insights and partner with product, business, engineering, and data stakeholders to define objectives and deliver reliable production models.

Requirements

• Bachelor’s degree or above in a quantitative field.
• 5+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 3+ years of industry experience with a PhD.
• Familiarity with advanced statistics, machine learning, optimization, and/or economics.
• Experience using Python and SQL with large datasets.
• Python experience in exploratory data analysis, predictive modeling, and applied machine learning using NumPy, pandas, scikit-learn, PyTorch, or similar libraries.
• Ability to communicate technical methodology as data narratives that drive decisions and strategy.
• Track record of shipping high-quality machine-learning products in production at scale.

Responsibilities

• Design, build, and optimize machine-learning models for credit-risk decisioning and portfolio management.
• Own the applied-science development lifecycle from data exploration through deployment, monitoring, and iteration.
• Investigate and integrate structured and unstructured data sources into credit models.
• Develop backtesting, validation, and monitoring frameworks for model performance and business impact.
• Apply machine learning, statistics, causal inference, optimization, and economics to business problems.
• Generate and communicate data-driven insights that influence product, risk, and company strategy.
• Partner with product, business, engineering, and data stakeholders to define objectives and an applied-science roadmap.
• Contribute to model-development, experimentation, documentation, testing, and production-reliability best practices.

Benefits

• Flexible PTO.
• Centralized home-office equipment ordering.
• Health and wellness stipend.
• Budget for intra-office travel.
• Weekly coffee stipend.
• 100% medical, dental, and vision insurance coverage for employees in the United States, with partial dependent coverage.
• One Medical annual membership.
• 401(k) with employer contribution match.
• Fertility HRA up to $10,000 per year.
• Paid parental leave.
• Pet insurance.
• In-office lunch, snacks, and drinks.
• Relocation support to New York City or San Francisco as needed.