Associate Data Science

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 opportunity 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
  

Requirements

Required Qualifications

• 2 to 5
  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)
  

Preferred Qualifications

• 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