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# Senior Applied AI & Data Scientist

- **Company:** NR Consulting LLC
- **Location:** New Haven, CT, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, ARM Architecture, Continuous Integration, IBM DB2, Python (Programming Language), PostgreSQL, Machine Learning, Tensorflow, Standard Sql, Search Technologies, Software Deployment, Talend, Enterprise Data Management, Pytorch, Retrieval-Augmented Generation, Flask (Web Framework), Large Language Models, Snowflake, Model Validation, Generative AI, Agentic-AI, Fastapi, AI Platforms, Scikit Learn, Xgboost, Machine Learning Operations, Cloud Migration, Docker
- **Published:** November 7, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/px8fw0a485

## About the Role

* 6+ years of experience in Data Science, Advanced Analytics, and Machine Learning, including production deployment.
 * 2+ years of enterprise GenAI/LLM experience (RAG, AI Agents, Evaluation, Guardrails).
 * Strong Python and SQL skills.
 * Hands-on Snowflake (Snowpark/Cortex) and PostgreSQL experience.
 * Experience with ML frameworks such as Scikit-learn, XGBoost, PyTorch, or TensorFlow.
 * Experience with Vector Databases, Embeddings, and Hybrid Search.
 * Experience deploying production AI services, APIs, monitoring, and CI/CD., * Financial services experience (Life Insurance, Annuities, or Investments).
 * Experience with IBM DB2, Talend, and legacy-to-cloud modernization.
 * Familiarity with MLflow, Docker, FastAPI, or Flask.

## Description

* Lead end-to-end AI/ML solution development, from data analysis to production deployment.
 * Build LLM, Generative AI, RAG, and Agentic AI solutions using enterprise data.
 * Develop machine learning and statistical models for insurance, actuarial, claims, risk, and investment functions.
 * Build feature pipelines, embeddings, vector search, and knowledge retrieval solutions.
 * Establish model evaluation, monitoring, performance tracking, and CI/CD processes.
 * Implement responsible AI practices, including bias detection, transparency, and governance.
 * Collaborate with business stakeholders, data engineers, and architects to deliver scalable AI solutions.