Sr.Data Scientist II

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Title: Sr. Data Scientist II

Location: Raleigh, NC

Duration: Long Term

Rate: W2

Only locals-F2F interview must

Description:

• Lead experimentation and model development for AI/ML solutions in legal products. 
• Design and evaluate NLP, LLM, and generative AI approaches (e.g., RAG, prompt strategies). 
• Define agentic workflows and reasoning strategies for multi-step legal tasks. 
• Define retrieval strategies, including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality). 
• Analyze large-scale legal datasets to extract insights and improve model performance. 
• Establish best practices for model evaluation, validation, and benchmarking. 
• Translate experimental results into clear product recommendations and business impact. 
• Collaborate with product, legal experts, and engineers to align solutions with user needs. 
• Mentor team members and provide technical leadership in data science and AI. 

Required Qualifications 

• 10+ years experience must

• Bachelor’s or Master’s degree in a relevant field (e.g., Computer Science, Data Science, Statistics, Mathematics). 
• Strong experience in machine learning, NLP, and LLM-based modeling. 
• Proven experience designing and running experiments, including model evaluation and iteration. 
• Experience with generative AI techniques (e.g., prompt engineering, RAG). 
• Experience designing and evaluating hybrid search (semantic + lexical) using embeddings and vector databases. 
• Experience designing agentic workflows and reasoning strategies, with hands-on experience applying agent frameworks (e.g., LangChain, LangGraph, AutoGen) in real-world use cases. 
• Proficiency in Python and data analysis tools. 
• Strong foundation in statistics, modeling, and large-scale text processing. 

 Preferred Qualifications 

• Experience in legal, regulatory, compliance, or risk analytics domains. 
• Familiarity with legal text corpora, citation networks, or legal research workflows. 
• Experience with graph databases (e.g., Dgraph, Neo4j, Neptune) and knowledge graph concepts. 
• Knowledge of responsible AI practices (e.g., explainability, bias mitigation). 
• Experience influencing product direction through data science insights.