Senior Applied AI & Data Scientist

NR Consulting LLC

New Haven, CT, United States

2 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

2 years minimum

Working hours

Regular working hours

Job source

arc.dev

Tech stack

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

+18 more

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

Job 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.

Requirements

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.

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