Job Summary

We're seeking 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 prospect to deploy production ML
systems at scale while working with rich, proprietary datasets in the fintech
space.

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