Full-Time Director, Data Science - Credit Risk

We are seeking an experienced Director, Data Science to lead data science projects focused on credit risk scoring. Expertise in building, implementing, and optimizing credit risk models (including machine learning) is essential, along with team management skills.

You'll be a hands-on coder, leveraging data-driven insights to guide decision-making. Experience in modeling and proficiency in Python are critical, along with a history of delivering solutions to complex credit risk challenges.

Responsibilities:

1. Build and mentor a team of data scientists specializing in credit risk modeling.
2. Develop the data science strategy for credit risk modeling and analytics aligned with company goals.
3. Collaborate with product, engineering, and compliance teams to integrate models into workflows.
4. Design scalable, accurate, and explainable credit risk models using machine learning and traditional statistical techniques, addressing problems across the entire credit lifecycle.
5. Write high-quality, production-grade Python code to prototype and implement models.
6. Ensure compliance with regulatory requirements and company policies.
7. Analyze large datasets to identify credit risk trends and drivers, providing actionable insights to partners.
8. Develop feature engineering and data enrichment approaches to improve model performance.
9. Maintain existing models, ensuring their accuracy and timeliness.
10. Communicate technical concepts and model outcomes effectively to non-technical partners.
11. Provide strategic insights to executive leadership based on data science outcomes.

Qualifications:

• Master's degree in Data Science, Statistics, Computer Science, Mathematics, or related field
• 8+ years of data science experience, focusing on credit risk modeling
• Experience leading teams
• Hands-on experience developing and deploying machine learning models, especially in credit risk contexts
• Fundamental knowledge of general processes (targeting, fraud detection, acquisitions, account management, collections)
• Advanced proficiency in Python and key libraries (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch)
• Knowledge of statistical modeling, feature engineering, and machine learning algorithms
• Experience with big data technologies and distributed systems (e.g., Spark, Hadoop)
• Knowledge of credit risk scoring methodologies, regulatory frameworks, and model governance
• Experience in data scraping and parsing unstructured data
• Strong communication, mentorship, and leadership skills
• Ability to translate complex technical details into actionable insights for diverse partners
• Experience with visualization tools like Tableau

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