Principal Data Scientist - Foundational Models

Responsibilities:

• Model Training & Deployment: Lead the training and production deployment of Windfall’s foundational models, focusing on predicting key financial metrics like household net worth and investable assets.
• Model Performance Analysis: Analyze and monitor the performance of machine learning models, ensuring they meet business goals and adhere to model risk management standards.
• Documentation & Communication: Document modeling processes with a high level of rigor and transparency. Effectively communicate key performance metrics and model insights to business stakeholders, ensuring clarity and alignment.
• Model Risk Management: Ensure compliance with OCC Model Risk Management standards, implementing processes for model validation, testing, and monitoring.
• Data Handling: Work with large, disparate sources of data to build robust and stable models that are resilient in real-world applications.
• Cross-Functional Collaboration: Partner closely with engineers, analysts, and business stakeholders to design and implement models that deliver actionable insights and drive business outcomes.

Requirements:

• At least 8 years experience working directly with machine learning models.
• Advanced proficiency in machine learning techniques and statistical modeling.
• Expert in Python, with a strong track record of building, deploying, and maintaining machine learning models at scale.
• Experience handling large, diverse datasets from multiple sources.
• Impeccable attention to detail in both model development and result reporting.
• Strong ability to communicate technical concepts and complex model outputs clearly and effectively to non-technical stakeholders.

Preferred Qualifications:

• Advanced degree (Master’s or PhD) in Data Science, Machine Learning, Statistics, Computer Science, or related fields.
• Experience specifically with predictive models for financial products (e.g., household net worth, investable assets, credit scoring).
• Experience with OCC model risk management practices, and working within highly regulated environments.
• Proven experience in putting large-scale machine learning models into production environments.
• Understanding of financial regulations and compliance, particularly around model risk management standards (e.g., OCC regulations).
• Familiarity with MLOps tools and processes to monitor and manage machine learning models in production.