Freelance Credit Risk Data Scientist – Machine Learning for Lending Products

Company Description

ThreatXIntel is a startup cyber security company dedicated to protecting businesses and organizations from evolving cyber threats. We specialize in providing tailored, cost-effective solutions in areas such as cloud security, web and mobile security testing, DevSecOps, and more. Our proactive approach ensures continuous monitoring and testing to identify vulnerabilities before they can be exploited. With a mission to deliver high-quality services, ThreatXIntel empowers businesses of all sizes to safeguard their digital assets effectively. Our team fosters peace of mind, allowing clients to focus on achieving their growth goals.

Role Description

We are seeking an experienced Credit Risk Data Scientist to design and implement advanced machine learning models for predicting credit risk across multiple short-term lending products , including tax refund advances, Buy Now Pay Later (BNPL) , and installment loans . This is a hands-on, data-driven role requiring both technical depth and business acumen to drive impactful lending decisions.

Key Responsibilities

• Design, build, evaluate, and defend machine learning models to predict credit risk across various lending products
• Collaborate with product and risk teams to align model outcomes with business goals and lending strategies
• Develop data pipelines for feature engineering, model training, scoring, and performance tracking using Python and SQL
• Deploy and monitor models in production environments , collaborating with engineering teams and other data scientists to ensure scalability and performance
• Work with stakeholders to formulate credit policies driven by insights from machine learning models
• Track and evaluate model performance using metrics like AUC, KS, Gini , and stability measures like PSI and CSI
• Ensure model fairness , interpretability , and compliance with regulatory frameworks (e.g., FCRA , ECOA )
• Maintain awareness of data privacy and ethical considerations related to credit risk models

Required Skills and Experience

Preferred Experience