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# Data Scientist II

- **Company:** Kforce Inc.
- **Location:** New York, NY, United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Business Logic, Information Extraction, Python (Programming Language), Machine Learning, Natural Language Processing, Regression Testing, Large Language Models, Data Pipelines
- **Published:** October 10, 2026
- **Apply:** https://www.kforce.com/find-work/search-jobs/#/detail/MTY5Nn5FUUd-MjE5MDk1NFQxfjk5/

## About the Role

* To be considered for this position, candidates must have experience in a similar role, or they must possess significant knowledge, experience, and abilities to successfully perform the responsibilities listed
 * Relevant education and/or training will be considered a plus
 * Experience with traditional machine learning is a strong plus

## Description

Kforce has a client that is seeking a Data Scientist II onsite in Raleigh, NC or remote EST. Summary: We are seeking a hands-on Data Scientist to develop and evaluate AI solutions for legal content workflows. The role focuses on large language models, natural language processing, and reliable data pipelines. The successful candidate will collaborate with engineers, product stakeholders, and legal subject matter experts to translate business requirements and editorial feedback into measurable improvements. This role requires strong Python skills, sound experimental judgment, and the ability to independently investigate issues and deliver working solutions. Duties:

 * Develop and improve LLM-based workflows for information extraction, classification, legal summarization, and content generation
 * Design and evaluate prompts, retrieval-augmented generation (RAG), and multi-step agent workflows
 * Build reliable data-preparation pipelines, including parsing, transformation, validation, caching, and recovery from interrupted processing
 * Create representative evaluation datasets and conduct controlled experiments, error analysis, and regression testing
 * Work with subject matter experts to assess factual accuracy, completeness, source support, and consistency, and translate feedback into generalizable improvements
 * Diagnose problems across data, retrieval, model behavior, and application logic
 * Balance output quality, latency, and cost when comparing technical approaches
 * Maintain tested, version-controlled code and clear documentation to support reproducibility and team handoffs