Director Data Science

Job Description

Job Summary

This is an opportunity to deploy production ML systems at scale while working with rich, proprietary datasets in the fintech space.

Core Responsibilities

• 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

• 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

• 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

• 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

• 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

• 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

Requirements

• 10 + years of experience in data science or machine learning
• Expert proficiency in Python and ML libraries (scikit-learn, PyTorch, TensorFlow)
• Experience deploying ML models to production environments
• Strong foundation in statistics, probability, and experimental design
• Experience with NLP and document processing techniques
• Proficiency with SQL and data manipulation at scale
• Experience with cloud ML platforms (AWS SageMaker, Azure ML, or GCP Vertex AI)
• PhD degree in Computer Science, Statistics, Mathematics, or related field (or equivalent experience)

• Experience in financial services, fintech, or SaaS environments
• Experience with LLMs, RAG systems, and agentic AI frameworks
• Master's or PhD in a quantitative field
• Experience with MLOps tools (MLflow, Kubeflow, Weights & Biases)
• Knowledge of computer vision or OCR for document processing
• Familiarity with compliance requirements in financial services