Job Description :

We're seeking a talented Data Scientist to build AI-powered capabilities that transform commercial real estate loan servicing. You'll work on cutting-edge problems spanning document intelligence, agentic AI systems, and predictive analytics that directly impact how billions of dollars in commercial loans are managed. This is an opportunity to deploy production ML systems at scale while working with rich, proprietary datasets in the fintech space.

Core Responsibilities :

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.