Director Data Science

Core Responsibilities

Model Development

• Design, train, and evaluate machine learning models for production use
• Conduct experiments and A/B tests to validate model improvements
• Implement model interpretability and explainability techniques
• Stay current with latest research and apply state-of-the-art methods

Production ML

• Collaborate with Engineering and infrastructure to deploy models to production
• Build data pipelines and feature engineering workflows
• Monitor model performance and implement retraining strategies
• Create APIs and interfaces for model predictions
• Optimize models for latency, throughput and cost

Data Analysis and Insights

• Perform exploratory data analysis
• Identify patterns and anomalies in commercial real estate data
• Communicate findings to product and business stakeholders
• Develop metrics and dashboards to track model performance
• Validate data quality and implement data validation

Document Intelligence and NLP

• Build document extraction and classification models for loan documents
• Develop NLP pipelines for processing unstructured financial text
• Implement OCR and document parsing solutions for automated data extraction

Agentic AI and LLM Systems

• Design and implement LLM-powered applications and agentic workflows
• Develop RAG (Retrieval-Augmented Generation) systems for document Q&A
• Implement prompt engineering strategies and LLM evaluation frameworks
• Build guardrails and safety mechanisms for AI-generated outputs

Collaboration and Support

• Partner with Product teams to translate business requirements into ML solutions
• Work with Data Engineers on data pipeline and feature store requirements
• Collaborate with Domain Experts to validate model outputs against business logic
• Document model architectures, experiments, and decision rationale
• Mentor junior data scientists and share best practices